Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

[1]  L. Shao,et al.  From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms , 2014, IEEE Transactions on Cybernetics.

[2]  C. Rother,et al.  Artificial-intelligence-driven scanning probe microscopy , 2020 .

[3]  Richard Walker,et al.  Emerging trends in peer review—a survey , 2015, Front. Neurosci..

[4]  Ying Zhang,et al.  Batch normalized recurrent neural networks , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Gang Niu,et al.  Analysis and Improvement of Policy Gradient Estimation , 2011, NIPS.

[6]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[7]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[8]  Matteo Fischetti,et al.  Embedded hyper-parameter tuning by Simulated Annealing , 2019, ArXiv.

[9]  Richard S. Zemel,et al.  Learning Latent Subspaces in Variational Autoencoders , 2018, NeurIPS.

[10]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[11]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[12]  Dabal Pedamonti,et al.  Comparison of non-linear activation functions for deep neural networks on MNIST classification task , 2018, ArXiv.

[13]  Haibin Ling,et al.  Salient Object Detection in the Deep Learning Era: An In-Depth Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Mengjie Zhang,et al.  A survey on evolutionary machine learning , 2019, Journal of the Royal Society of New Zealand.

[15]  Stefano Cagnoni,et al.  Biomedical image segmentation using geometric deformable models and metaheuristics , 2015, Comput. Medical Imaging Graph..

[16]  Yann LeCun,et al.  Orthogonal RNNs and Long-Memory Tasks , 2016, ArXiv.

[17]  Peter M. Roth,et al.  L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[18]  Yi-Hsuan Yang,et al.  Towards a Deeper Understanding of Adversarial Losses , 2019, ArXiv.

[19]  Wookey Lee,et al.  Prior Art Search Using Multi-modal Embedding of Patent Documents , 2020, 2020 IEEE International Conference on Big Data and Smart Computing (BigComp).

[20]  Martin Szomszor,et al.  Dimensions - A Collaborative Approach to Enhancing Research Discovery , 2018 .

[21]  Pascal Vincent,et al.  Visualizing Higher-Layer Features of a Deep Network , 2009 .

[22]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[23]  John Tran,et al.  cuDNN: Efficient Primitives for Deep Learning , 2014, ArXiv.

[24]  Xiaofei Wang,et al.  A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head , 2018, Scientific Reports.

[25]  Surya Bowyer,et al.  The Wayback Machine: notes on a re-enchantment , 2020, Archival Science.

[26]  Kyunghyun Cho,et al.  Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images , 2013, ICML.

[27]  Randall D. Beer,et al.  The dynamics of adaptive behavior: A research program , 1997, Robotics Auton. Syst..

[28]  Richard S. Zemel,et al.  Aggregated Momentum: Stability Through Passive Damping , 2018, ICLR.

[29]  Jun Liang,et al.  Residual Recurrent Neural Networks for Learning Sequential Representations , 2018, Inf..

[30]  W. Wisniewski,et al.  Y2O3–Al2O3 microsphere crystallization analyzed by electron backscatter diffraction (EBSD) , 2020, Scientific Reports.

[31]  Olivier Bachem,et al.  Recent Advances in Autoencoder-Based Representation Learning , 2018, ArXiv.

[32]  Erik Meijer,et al.  Gradient Descent: The Ultimate Optimizer , 2019, ArXiv.

[33]  Shuang Wu,et al.  Convolution with even-sized kernels and symmetric padding , 2019, NeurIPS.

[34]  Mohamed Saber Naceur,et al.  Reinforcement learning for neural architecture search: A review , 2019, Image Vis. Comput..

[35]  Eriko Nurvitadhi,et al.  Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? , 2017, FPGA.

[36]  Fathi M. Salem,et al.  Simplified minimal gated unit variations for recurrent neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[37]  Tae-Wuk Bae,et al.  Spatial and temporal bilateral filter for infrared small target enhancement , 2014 .

[38]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[39]  James Bailey,et al.  Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems , 2019, Pattern Recognit..

[40]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[41]  Tong Tong,et al.  Image Super-Resolution Using Dense Skip Connections , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[42]  Carla P. Gomes,et al.  Understanding Batch Normalization , 2018, NeurIPS.

[43]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[44]  Chulhong Kim,et al.  Deep learning-based speed of sound aberration correction in photoacoustic images , 2020, BiOS.

[45]  Jianyu Huang,et al.  Quantitative comparison between real space and Bloch wave methods in image simulation. , 2017, Micron.

[46]  K. Ishizuka Multislice formula for inclined illumination , 1982 .

[47]  Lingfan Yu,et al.  Low latency RNN inference with cellular batching , 2018, EuroSys.

[48]  Jian Yang,et al.  Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Xiaoqi Li,et al.  Towards Accurate High Resolution Satellite Image Semantic Segmentation , 2019, IEEE Access.

[50]  Tianqi Chen,et al.  Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.

[51]  Muhammad Haris,et al.  Application of deep learning for retinal image analysis: A review , 2020, Comput. Sci. Rev..

[52]  Alexander D'Amour,et al.  Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift , 2020, ArXiv.

[53]  Xin Yang,et al.  Towards Automated Semantic Segmentation in Prenatal Volumetric Ultrasound , 2019, IEEE Transactions on Medical Imaging.

[54]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[55]  Shabir Ahmad,et al.  Evaluation of Search Engines Using Advanced Search: Comparative Analysis of Yahoo and Bing , 2019 .

[56]  Jessica Bates,et al.  Will Web Search Engines Replace Bibliographic Databases in the Systematic Identification of Research , 2017 .

[57]  Dustin Tran,et al.  TensorFlow Distributions , 2017, ArXiv.

[58]  Hidetaka Sawada,et al.  Visualization of Light Elements at Ultrahigh Resolution by STEM Annular Bright Field Microscopy , 2009, Microscopy and Microanalysis.

[59]  Ankit Singh Rawat,et al.  Can gradient clipping mitigate label noise? , 2020, ICLR.

[60]  Daniel E. Acuna,et al.  The effect of novelty on the future impact of scientific grants , 2019, ArXiv.

[61]  Zhi Zhang,et al.  Bag of Tricks for Image Classification with Convolutional Neural Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  M. Lei,et al.  Machine learning in materials science , 2019, InfoMat.

[63]  Stefano Cozzini,et al.  The first annotated set of scanning electron microscopy images for nanoscience , 2018, Scientific Data.

[64]  Claudio Gallicchio,et al.  Deep Echo State Network (DeepESN): A Brief Survey , 2017, ArXiv.

[65]  A. Laub,et al.  The singular value decomposition: Its computation and some applications , 1980 .

[66]  Klaus-Robert Müller,et al.  Towards Explainable Artificial Intelligence , 2019, Explainable AI.

[67]  M. Snir,et al.  Channel and filter parallelism for large-scale CNN training , 2019, SC.

[68]  Joseph B. Lambert,et al.  Nuclear magnetic resonance spectroscopy : an introduction to principles, applications, and experimental methods , 2004 .

[69]  Avrim Blum,et al.  Learning Complexity of Simulated Annealing , 2020, ArXiv.

[70]  G. Nolze,et al.  Physics-based simulation models for EBSD: advances and challenges , 2015, 1505.07982.

[71]  Ivan Lazić,et al.  Phase contrast scanning transmission electron microscopy imaging of light and heavy atoms at the limit of contrast and resolution , 2018, Scientific Reports.

[72]  Laurens van der Maaten,et al.  Barnes-Hut-SNE , 2013, ICLR.

[73]  B. Tittmann,et al.  Review of Progress in Atomic Force Microscopy , 2018, The Open Neuroimaging Journal.

[74]  David W. Aha,et al.  DARPA's Explainable Artificial Intelligence (XAI) Program , 2019, AI Mag..

[75]  Bo Wang,et al.  High-throughput, algorithmic determination of pore parameters from electron microscopy , 2020 .

[76]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[77]  Yongdong Zhang,et al.  Deep Hashing Based on VAE-GAN for Efficient Similarity Retrieval , 2019 .

[78]  Alexander Novikov,et al.  Tensorizing Neural Networks , 2015, NIPS.

[79]  Uwe Hohenstein,et al.  ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations , 2019, 2019 IEEE International Conference on Software Architecture Companion (ICSA-C).

[80]  Yu Liu,et al.  A review of semantic segmentation using deep neural networks , 2017, International Journal of Multimedia Information Retrieval.

[81]  Gonzalo Génova,et al.  The Problem Is Not Professional Publishing, But the Publish-or-Perish Culture , 2018, Science and Engineering Ethics.

[82]  I. Díaz,et al.  Cs‐Corrected STEM Imaging of both Pure and Silver‐Supported Metal‐Organic Framework MIL‐100(Fe) , 2017 .

[83]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[84]  Daniel Omeiza,et al.  Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models , 2019, ArXiv.

[85]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[86]  Rohan Ramanath,et al.  An Attentive Survey of Attention Models , 2019, ACM Trans. Intell. Syst. Technol..

[87]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[88]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[89]  Shuiwang Ji,et al.  Adaptive Convolutional ReLUs , 2020, AAAI.

[90]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[91]  Peter L. Bartlett,et al.  RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning , 2016, ArXiv.

[92]  Shivani Kaushal,et al.  Survey on Neural Machine Translation for multilingual translation system , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).

[93]  Victor Talpaert,et al.  Deep Reinforcement Learning for Autonomous Driving: A Survey , 2020, IEEE Transactions on Intelligent Transportation Systems.

[94]  Petter Nielsen,et al.  Predatory journals: A sign of an unhealthy publish or perish game? , 2020, Inf. Syst. J..

[95]  Michael James,et al.  Online Normalization for Training Neural Networks , 2019, NeurIPS.

[96]  Chen Sun,et al.  Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[97]  Moritz Schubotz,et al.  Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations , 2019, 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL).

[98]  Kaylen J Pfisterer,et al.  Fully-Automatic Semantic Segmentation for Food Intake Tracking in Long-Term Care Homes , 2019, ArXiv.

[99]  Eugenio Culurciello,et al.  Flattened Convolutional Neural Networks for Feedforward Acceleration , 2014, ICLR.

[100]  Gui-Bin Bian,et al.  Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications , 2018, IEEE Access.

[101]  Surya Ganguli,et al.  On the Expressive Power of Deep Neural Networks , 2016, ICML.

[102]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[103]  Trevor Darrell,et al.  Simultaneous Deep Transfer Across Domains and Tasks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[104]  Andrea Vedaldi,et al.  There and Back Again: Revisiting Backpropagation Saliency Methods , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[105]  Senthil Mani,et al.  DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers , 2018, AAAI.

[106]  Karen O. Egiazarian,et al.  Single Image Super-Resolution Based on Wiener Filter in Similarity Domain , 2017, IEEE Transactions on Image Processing.

[107]  P. Camus,et al.  Benefits from bremsstrahlung distribution evaluation to get unknown information from specimen in SEM and TEM , 2018 .

[108]  E. Bauer LEEM, SPLEEM and SPELEEM , 2019, Springer Handbook of Microscopy.

[109]  Gabriel Goh,et al.  Why Momentum Really Works , 2017 .

[110]  Brijendra Kumar Joshi,et al.  A Review Paper: Noise Models in Digital Image Processing , 2015, ArXiv.

[111]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[112]  Alessandro Ecclesie Agazzi Study of the usability of LinkedIn: a social media platform meant to connect employers and employees , 2020, ArXiv.

[113]  Xiang Li,et al.  Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning , 2019, Computer Methods in Applied Mechanics and Engineering.

[114]  Deborah E. Rupp,et al.  Answers to 18 Questions About Open Science Practices , 2019 .

[115]  N. C. MacDonald,et al.  Auger Electron Spectroscopy in the Scanning Electron Microscope: Auger Electron Images , 1971 .

[116]  Mubarak Shah,et al.  Norm-Preservation: Why Residual Networks Can Become Extremely Deep? , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[118]  Suman Jana,et al.  Certified Robustness to Adversarial Examples with Differential Privacy , 2018, 2019 IEEE Symposium on Security and Privacy (SP).

[119]  Jianjun Lei,et al.  Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.

[120]  A Gómez-Rodríguez,et al.  SimulaTEM: multislice simulations for general objects. , 2010, Ultramicroscopy.

[121]  Hossam M. Kasem,et al.  A comparison study for image compression based on compressive sensing , 2020, International Conference on Graphic and Image Processing.

[122]  Simon Portegies Zwart,et al.  Newton vs the machine: solving the chaotic three-body problem using deep neural networks , 2019, ArXiv.

[123]  Jorge Nocedal,et al.  On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.

[124]  Leslie Lamport,et al.  LaTeX - A Document Preparation System: User's Guide and Reference Manual, Second Edition , 1994 .

[125]  J. Israel Martínez-López,et al.  Which Are the Tools Available for Scholars? A Review of Assisting Software for Authors during Peer Reviewing Process , 2019, Publ..

[126]  Philipp Probst,et al.  Hyperparameters and tuning strategies for random forest , 2018, WIREs Data Mining Knowl. Discov..

[127]  Nikhil Ketkar,et al.  Deep Learning with Python , 2017 .

[128]  Yue Zhang,et al.  Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring , 2017, CoNLL.

[129]  Eirik Endeve,et al.  Dynamic scan control in STEM: spiral scans , 2016, Advanced Structural and Chemical Imaging.

[130]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[131]  I. Rabi,et al.  A New Method of Measuring Nuclear Magnetic Moment , 1938 .

[132]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[133]  Brent J. Hecht,et al.  Measuring the Importance of User-Generated Content to Search Engines , 2019, ICWSM.

[134]  Elad Hoffer,et al.  Norm matters: efficient and accurate normalization schemes in deep networks , 2018, NeurIPS.

[135]  Ankur Bapna,et al.  The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation , 2018, ACL.

[136]  Brian McMahon,et al.  CIF: the computer language of crystallography. , 2002, Acta crystallographica. Section B, Structural science.

[137]  Jungwon Lee,et al.  Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition , 2017, INTERSPEECH.

[138]  Yo Joong Choe,et al.  Probabilistic Interpretations of Recurrent Neural Networks , 2017 .

[139]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[140]  Helen Yannakoudakis,et al.  Automatic Text Scoring Using Neural Networks , 2016, ACL.

[141]  Xiaoyan Liu,et al.  The Deep Learning Compiler: A Comprehensive Survey , 2020, IEEE Transactions on Parallel and Distributed Systems.

[142]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[143]  Gang Chen,et al.  Effective and Efficient Dropout for Deep Convolutional Neural Networks , 2019, ArXiv.

[144]  W. Zuo,et al.  Deep Learning on Image Denoising: An overview , 2019, Neural Networks.

[145]  Bart Goris,et al.  Development of a fast electromagnetic beam blanker for compressed sensing in scanning transmission electron microscopy , 2015, 1509.06656.

[146]  Kenta Oono,et al.  Chainer : a Next-Generation Open Source Framework for Deep Learning , 2015 .

[147]  Thed N. van Leeuwen,et al.  Open Access uptake by universities worldwide , 2020, PeerJ.

[148]  Seong Joon Oh,et al.  Exploiting Saliency for Object Segmentation from Image Level Labels , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[149]  Mohammad Sohel Rahman,et al.  MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation , 2019, Neural Networks.

[150]  Ajmal Mian,et al.  Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.

[151]  P. Nellist,et al.  HAADF-STEM imaging with sub-angstrom probes: a full Bloch wave analysis. , 2004, Journal of electron microscopy.

[152]  Mykhaylo Yatsymirskyy,et al.  Effectiveness of Fast Fourier Transform implementations on GPU and CPU , 2015, 2015 16th International Conference on Computational Problems of Electrical Engineering (CPEE).

[153]  Jugal K. Kalita,et al.  A Survey of the Usages of Deep Learning for Natural Language Processing , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[154]  T. Latychevskaia Spatial coherence of electron beams from field emitters and its effect on the resolution of imaged objects. , 2017, Ultramicroscopy.

[155]  Yang You,et al.  Scaling SGD Batch Size to 32K for ImageNet Training , 2017, ArXiv.

[156]  Shadrokh Samavi,et al.  Modeling of Pruning Techniques for Deep Neural Networks Simplification , 2020, ArXiv.

[157]  Pardeep Kumar,et al.  Wavelet Packet Based CT Image Denoising Using Bilateral Method and Bayes Shrinkage Rule , 2019, Handbook of Multimedia Information Security.

[158]  McKenzie Raub,et al.  Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices , 2018 .

[159]  Oge Marques,et al.  Dropout vs. batch normalization: an empirical study of their impact to deep learning , 2020, Multimedia Tools and Applications.

[160]  Bodo B. Schlegelmilch,et al.  Global social networking sites and global identity: A three-country study , 2019 .

[161]  Zhongmin Zhang,et al.  Evaluating the effectiveness of Web search engines on results diversification , 2019, Inf. Res..

[162]  Mark van den Brand,et al.  DeepClone: Modeling Clones to Generate Code Predictions , 2020, ICSR.

[163]  Ping Wang,et al.  Adversarial Noise Layer: Regularize Neural Network by Adding Noise , 2018, 2019 IEEE International Conference on Image Processing (ICIP).

[164]  Jie Liu,et al.  3D deep encoder-decoder network for fluorescence molecular tomography. , 2019, Optics letters.

[165]  Alexander Mordvintsev,et al.  Inceptionism: Going Deeper into Neural Networks , 2015 .

[166]  Mourad Ouzzani,et al.  Data Curation with Deep Learning [Vision]: Towards Self Driving Data Curation , 2018, ArXiv.

[167]  Q. Ramasse Twenty years after: How "Aberration correction in the STEM" truly placed a "A synchrotron in a Microscope". , 2017, Ultramicroscopy.

[169]  Bernd Bischl,et al.  Tunability: Importance of Hyperparameters of Machine Learning Algorithms , 2018, J. Mach. Learn. Res..

[170]  Jie Liu,et al.  Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN , 2018, ArXiv.

[171]  Dhabaleswar K. Panda,et al.  An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures , 2017, MLHPC@SC.

[172]  Anthony K. H. Tung,et al.  SINGA: A Distributed Deep Learning Platform , 2015, ACM Multimedia.

[173]  David B. Williams,et al.  Transmission Electron Microscopy: Diffraction, Imaging, and Spectrometry , 2016 .

[174]  Jeffrey M. Ede Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder , 2018, Ultramicroscopy.

[175]  Matthias Zwicker,et al.  Dual-domain image denoising , 2013, 2013 IEEE International Conference on Image Processing.

[176]  BabarMuhammad Ali,et al.  Deep Learning for Source Code Modeling and Generation , 2020 .

[177]  C. D. Jaidhar,et al.  Applicability of machine learning in spam and phishing email filtering: review and approaches , 2020, Artificial Intelligence Review.

[178]  Jaehoon Lee,et al.  Wide neural networks of any depth evolve as linear models under gradient descent , 2019, NeurIPS.

[179]  David C. Joy,et al.  Scanning Electron Microscopy and X-Ray Microanalysis , 2017 .

[180]  B. Taylor,et al.  CODATA recommended values of the fundamental physical constants: 2006 | NIST , 2007, 0801.0028.

[181]  Marcelo Bertalmío,et al.  FLUID DYNAMICS, AND IMAGE AND VIDEO INPAINTING , 2001 .

[182]  Tuo Zhao,et al.  Towards Understanding the Importance of Noise in Training Neural Networks , 2019, ICML 2019.

[183]  Vitoantonio Bevilacqua,et al.  A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images , 2019, BMC Medical Informatics and Decision Making.

[184]  Vinayak P. Dravid,et al.  Suppressing Electron Exposure Artifacts: An Electron Scanning Paradigm with Bayesian Machine Learning , 2016, Microscopy and Microanalysis.

[185]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[186]  Hongliang Ren,et al.  Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges , 2019, Robotics.

[187]  Sònia Estradé,et al.  Clustering analysis strategies for electron energy loss spectroscopy (EELS). , 2018, Ultramicroscopy.

[188]  J. O'Doherty,et al.  Reward representations and reward-related learning in the human brain: insights from neuroimaging , 2004, Current Opinion in Neurobiology.

[189]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[190]  Marc'Aurelio Ranzato,et al.  Learning Longer Memory in Recurrent Neural Networks , 2014, ICLR.

[191]  Guanhua Wang,et al.  Exemplar-based image inpainting using angle-aware patch matching , 2019, EURASIP Journal on Image and Video Processing.

[192]  Xiangru Lian,et al.  Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization , 2019, AISTATS.

[193]  Apostol Natsev,et al.  YouTube-8M: A Large-Scale Video Classification Benchmark , 2016, ArXiv.

[194]  Xingxing Zhang,et al.  A review of reinforcement learning methodologies for controlling occupant comfort in buildings , 2019, Sustainable Cities and Society.

[195]  Dong Yu,et al.  Single-channel mixed speech recognition using deep neural networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[196]  Bradley J. Erickson,et al.  Toolkits and Libraries for Deep Learning , 2017, Journal of Digital Imaging.

[197]  Leslie Lamport,et al.  LATEX. A document preparation system. User's Guide and Reference Manual , 1996 .

[198]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[199]  Adarsh Sehgal,et al.  Deep Reinforcement Learning Using Genetic Algorithm for Parameter Optimization , 2019, 2019 Third IEEE International Conference on Robotic Computing (IRC).

[201]  K. Ishizuka Prospects of atomic resolution imaging with an aberration-corrected STEM. , 2001, Journal of electron microscopy.

[202]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[203]  E. Kawalec-Latała Edge Detection on Images of Pseudoimpedance Section Supported by Context and Adaptive Transformation Model Images , 2014 .

[204]  Levent Sagun,et al.  Scaling description of generalization with number of parameters in deep learning , 2019, Journal of Statistical Mechanics: Theory and Experiment.

[205]  Inman Harvey,et al.  Seeing the light: artificial evolution, real vision , 1994 .

[206]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[207]  Boris Polyak Some methods of speeding up the convergence of iteration methods , 1964 .

[208]  P. N. Druzhkov,et al.  A survey of deep learning methods and software tools for image classification and object detection , 2016, Pattern Recognition and Image Analysis.

[209]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[210]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[211]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[212]  Lirong Zheng,et al.  Automated trading systems statistical and machine learning methods and hardware implementation: a survey , 2018, Enterp. Inf. Syst..

[213]  Shaozhang Niu,et al.  A Detection Approach Using LSTM-CNN for Object Removal Caused by Exemplar-Based Image Inpainting , 2020, Electronics.

[214]  Saeid Nahavandi,et al.  Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications , 2018, IEEE Transactions on Cybernetics.

[215]  Qian He,et al.  Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning , 2019, ArXiv.

[216]  Nathan Srebro,et al.  The Marginal Value of Adaptive Gradient Methods in Machine Learning , 2017, NIPS.

[217]  Eran Yahav,et al.  On the Practical Computational Power of Finite Precision RNNs for Language Recognition , 2018, ACL.

[218]  E. Robinson,et al.  PRINCIPLES OF DIGITAL WIENER FILTERING , 1967 .

[219]  George Kurian,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.

[220]  Leslie Pack Kaelbling,et al.  Generalization in Deep Learning , 2017, ArXiv.

[221]  Torsten Hoefler,et al.  Demystifying Parallel and Distributed Deep Learning , 2018, ACM Comput. Surv..

[222]  Ali Borji,et al.  Saliency Prediction in the Deep Learning Era: Successes and Limitations , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[223]  L. Bottou Curiously Fast Convergence of some Stochastic Gradient Descent Algorithms , 2009 .

[224]  Aleksander Madry,et al.  How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) , 2018, NeurIPS.

[225]  Mathukumalli Vidyasagar,et al.  An Introduction to Compressed Sensing , 2019 .

[226]  Jianxin Wu,et al.  Minimal gated unit for recurrent neural networks , 2016, International Journal of Automation and Computing.

[227]  Maxime Pelcat,et al.  Why TanH is a Hardware Friendly Activation Function for CNNs , 2017, ICDSC.

[228]  C. T. Koch,et al.  Hybridization approach to in-line and off-axis (electron) holography for superior resolution and phase sensitivity , 2014, Scientific Reports.

[229]  Arild Nøkland,et al.  Shifting Mean Activation Towards Zero with Bipolar Activation Functions , 2017, ICLR.

[230]  T. Matsuda,et al.  Observation of the magnetic flux and three-dimensional structure of skyrmion lattices by electron holography. , 2014, Nature nanotechnology.

[231]  Barak A. Pearlmutter,et al.  Automatic differentiation in machine learning: a survey , 2015, J. Mach. Learn. Res..

[232]  Lihi Zelnik-Manor,et al.  XNAS: Neural Architecture Search with Expert Advice , 2019, NeurIPS.

[233]  Timothy Baldwin,et al.  An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation , 2016, Rep4NLP@ACL.

[234]  Stefan Roth,et al.  Single-Stage Semantic Segmentation From Image Labels , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[235]  Shuai Li,et al.  Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[236]  Ilya Kostrikov,et al.  PlaNet - Photo Geolocation with Convolutional Neural Networks , 2016, ECCV.

[237]  Daisuke Kihara,et al.  Navigating 3D electron microscopy maps with EM-SURFER , 2015, BMC Bioinformatics.

[238]  Fabrizio Marozzo,et al.  Infrastructures for High-Performance Computing: Cloud Infrastructures , 2019, Encyclopedia of Bioinformatics and Computational Biology.

[239]  Dong-Hyun Lee,et al.  Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .

[240]  Kun Zhang,et al.  Twin Auxilary Classifiers GAN , 2019, NeurIPS.

[241]  G. Golub,et al.  Eigenvalue computation in the 20th century , 2000 .

[242]  Ifan G. Hughes,et al.  Measurements and their Uncertainties: A practical guide to modern error analysis , 2010 .

[243]  Saulius Gražulis,et al.  COD::CIF::Parser: an error-correcting CIF parser for the Perl language , 2016, Journal of applied crystallography.

[244]  Sham M. Kakade,et al.  The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure , 2019, NeurIPS.

[245]  Shiliang Sun,et al.  Multiview Machine Learning , 2019, Springer Singapore.

[246]  Christian Ledig,et al.  Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize , 2017, ArXiv.

[247]  Emre Ugur,et al.  Generalization in Transfer Learning , 2019, ArXiv.

[248]  Stewart A. Koppell,et al.  Design for a 10 keV multi-pass transmission electron microscope. , 2019, Ultramicroscopy.

[249]  J. Frank Electron Tomography , 1992, Springer US.

[250]  Quoc V. Le,et al.  Massive Exploration of Neural Machine Translation Architectures , 2017, EMNLP.

[251]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[252]  Olamilekan Shobayo,et al.  Data mining approach for predicting the daily Internet data traffic of a smart university , 2019, Journal of Big Data.

[253]  Shane Legg,et al.  Noisy Networks for Exploration , 2017, ICLR.

[254]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[255]  Sung-Bae Cho,et al.  Radial basis function neural networks: a topical state-of-the-art survey , 2016, Open Comput. Sci..

[256]  Steven R. Young,et al.  Optimizing deep learning hyper-parameters through an evolutionary algorithm , 2015, MLHPC@SC.

[257]  A. Kohn,et al.  Measuring the mean inner potential of Al2O3 sapphire using off-axis electron holography. , 2019, Ultramicroscopy.

[258]  Chris Yakopcic,et al.  A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.

[259]  Roberto Di Cosmo,et al.  Software Heritage: Why and How to Preserve Software Source Code , 2017, iPRES.

[260]  Ivan Oseledets,et al.  Tensor-Train Decomposition , 2011, SIAM J. Sci. Comput..

[261]  Mirit Kaldas,et al.  Journal impact factor: a bumpy ride in an open space , 2019, Journal of Investigative Medicine.

[262]  Tao Lei,et al.  A review of Convolutional-Neural-Network-based action recognition , 2019, Pattern Recognit. Lett..

[263]  Shin Ishii,et al.  UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images , 2019, Scientific Reports.

[264]  Wei Yu,et al.  A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends , 2018, IEEE Access.

[265]  M. K. Soni,et al.  Design of FPGA based 32-bit Floating Point Arithmetic Unit and verification of its VHDL code using MATLAB , 2014 .

[266]  Jiangbin Wu,et al.  Review on the Raman spectroscopy of different types of layered materials. , 2016, Nanoscale.

[267]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2002, IEEE Trans. Image Process..

[268]  Vijay S. Pande,et al.  Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT) , 2018, 1809.02723.

[269]  Mounia Mikram,et al.  A CNN-BiLSTM Model for Document-Level Sentiment Analysis , 2019, Mach. Learn. Knowl. Extr..

[270]  Matthew M Nowell,et al.  A Review of Strain Analysis Using Electron Backscatter Diffraction , 2011, Microscopy and Microanalysis.

[271]  F. Allen,et al.  The crystallographic information file (CIF) : a new standard archive file for crystallography , 1991 .

[272]  Yang Xu,et al.  Automated Essay Scoring based on Two-Stage Learning , 2019, ArXiv.

[273]  Li Zhao,et al.  Learning Structured Representation for Text Classification via Reinforcement Learning , 2018, AAAI.

[274]  Alex Graves,et al.  Neural Turing Machines , 2014, ArXiv.

[275]  Xianlong Wei,et al.  Direct Observation of the Layer-by-Layer Growth of ZnO Nanopillar by In situ High Resolution Transmission Electron Microscopy , 2017, Scientific Reports.

[276]  Kunihiko Fukushima,et al.  Neocognitron: A hierarchical neural network capable of visual pattern recognition , 1988, Neural Networks.

[277]  T. Morimura,et al.  Bloch-wave-based STEM image simulation with layer-by-layer representation. , 2009, Ultramicroscopy.

[278]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[279]  Jinat Ara,et al.  A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques , 2018 .

[280]  Matthew Quinn,et al.  Evolving Communication without Dedicated Communication Channels , 2001, ECAL.

[281]  Qian Du,et al.  Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[282]  M. Kunitski,et al.  Double-slit photoelectron interference in strong-field ionization of the neon dimer , 2018, Nature Communications.

[283]  AnHai Doan,et al.  Data Curation with Deep Learning , 2020, EDBT.

[284]  Jianfeng Zhan,et al.  Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks , 2017, ICANN.

[285]  Ajit Kembhavi,et al.  CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy , 2020, bioRxiv.

[286]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[287]  Miryung Kim,et al.  Are Code Examples on an Online Q&A Forum Reliable?: A Study of API Misuse on Stack Overflow , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).

[288]  M. Tanaka,et al.  Convergent-beam electron diffraction. , 1994, Journal of electron microscopy.

[289]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[290]  Jeffrey M. Ede,et al.  Adaptive learning rate clipping stabilizes learning , 2019, Mach. Learn. Sci. Technol..

[291]  Stefano Soatto,et al.  Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence , 2019, NeurIPS.

[292]  Sagar Jambhorkar,et al.  A Literature Review on Patent Information Retrieval Techniques , 2017 .

[293]  Marion Smits,et al.  DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data , 2020, Neuroinformatics.

[294]  Sebastian Raschka,et al.  Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning , 2018, ArXiv.

[295]  Jakob Grue Simonsen,et al.  A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion , 2015, CIKM.

[296]  L. Bendersky,et al.  Electron Diffraction Using Transmission Electron Microscopy , 2001, Journal of research of the National Institute of Standards and Technology.

[297]  Shaohui Liu,et al.  Medical image denoising using convolutional neural network: a residual learning approach , 2017, The Journal of Supercomputing.

[298]  Catherine D. Schuman,et al.  167-PFlops Deep Learning for Electron Microscopy: From Learning Physics to Atomic Manipulation , 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.

[299]  Alex Graves,et al.  Practical Variational Inference for Neural Networks , 2011, NIPS.

[300]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[301]  M. J. D. Powell,et al.  Direct search algorithms for optimization calculations , 1998, Acta Numerica.

[302]  Hyo-Eun Kim,et al.  Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks , 2018, NeurIPS.

[303]  Timothée Masquelier,et al.  Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition , 2015, Scientific Reports.

[304]  R. Downs,et al.  The American Mineralogist crystal structure database , 2003 .

[305]  Satoshi Matsuoka,et al.  An efficient, model-based CPU-GPU heterogeneous FFT library , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[306]  Yoshua Bengio,et al.  Hierarchical Multiscale Recurrent Neural Networks , 2016, ICLR.

[307]  B. Voigtländer,et al.  Invited Review Article: Multi-tip scanning tunneling microscopy: Experimental techniques and data analysis. , 2018, The Review of scientific instruments.

[308]  Verónica Bolón-Canedo,et al.  A scalable saliency-based Feature selection method with instance level information , 2019, Knowl. Based Syst..

[309]  Luciano Bononi,et al.  Reinforcement Learning-Based Spectrum Management for Cognitive Radio Networks: A Literature Review and Case Study , 2019, Handbook of Cognitive Radio.

[310]  Samuel S. Schoenholz,et al.  Mean Field Residual Networks: On the Edge of Chaos , 2017, NIPS.

[311]  Yiran Chen,et al.  A Survey of Accelerator Architectures for Deep Neural Networks , 2020 .

[312]  David Silver,et al.  Learning values across many orders of magnitude , 2016, NIPS.

[313]  Twan van Laarhoven,et al.  L2 Regularization versus Batch and Weight Normalization , 2017, ArXiv.

[314]  Minsuk Kahng,et al.  CNN 101: Interactive Visual Learning for Convolutional Neural Networks , 2020, CHI Extended Abstracts.

[315]  M. Jenkins,et al.  Characterisation of Radiation Damage by Transmission Electron Microscopy , 2000 .

[316]  S M Shafi,et al.  Retrieval performance of select search engines in the field of physical sciences , 2019 .

[317]  Jimin Liang,et al.  Medical Image Segmentation based on U-Net: A Review , 2020, Journal of Imaging Science and Technology.

[318]  Klaus C. J. Dietmayer,et al.  Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges , 2019, IEEE Transactions on Intelligent Transportation Systems.

[319]  R. Graczyk The eye. , 1955, Radiography.

[320]  Myriam Tami,et al.  An Overview of Deep Semi-Supervised Learning , 2020, ArXiv.

[321]  Wei Dai,et al.  Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms , 2017, Nature Methods.

[322]  Jamshid Bagherzadeh,et al.  A review of various semi-supervised learning models with a deep learning and memory approach , 2018, Iran Journal of Computer Science.

[323]  P. Griffiths Fourier Transform Infrared Spectrometry , 2007 .

[324]  Peter de Boves Harrington,et al.  Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes , 2018, Critical reviews in analytical chemistry.

[325]  Dong Yul Oh,et al.  Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm , 2020, Korean journal of radiology.

[326]  Aleksandr Romanov,et al.  Application of Natural Language Processing Algorithms to the Task of Automatic Classification of Russian Scientific Texts , 2019, Data Sci. J..

[327]  Zhiyong Liu,et al.  A Consensus Framework of Distributed Multiple-Tilt Reconstruction in Electron Tomography. , 2019, Journal of computational biology : a journal of computational molecular cell biology.

[328]  Roland Vollgraf,et al.  Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.

[329]  Stephen J. Pennycook,et al.  Scanning transmission electron microscopy : imaging and analysis , 2011 .

[330]  Yuichi Yoshida,et al.  Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.

[331]  E. Kirkland Computation in electron microscopy. , 2016, Acta crystallographica. Section A, Foundations and advances.

[332]  L. Vincze,et al.  Atomic spectrometry update – a review of advances in X-ray fluorescence spectrometry and its special applications , 2021, Journal of Analytical Atomic Spectrometry.

[333]  Robert Keyse Introduction to Scanning Transmission Electron Microscopy , 1997 .

[334]  Tianqi Chen,et al.  Training Deep Nets with Sublinear Memory Cost , 2016, ArXiv.

[335]  Soon Ki Jung,et al.  Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation , 2018, Neural Networks.

[336]  L. Vincze,et al.  2020 atomic spectrometry update – a review of advances in X-ray fluorescence spectrometry and its special applications , 2020, Journal of Analytical Atomic Spectrometry.

[337]  Nasser Kehtarnavaz,et al.  A Review of Multi-Objective Deep Learning Speech Denoising Methods , 2020, Speech Commun..

[338]  Haruna Chiroma,et al.  Machine learning for email spam filtering: review, approaches and open research problems , 2019, Heliyon.

[339]  Antonio J. Plaza,et al.  Image Segmentation Using Deep Learning: A Survey , 2021, IEEE transactions on pattern analysis and machine intelligence.

[340]  Daniel Wolverson,et al.  Raman Techniques: Fundamentals and Frontiers , 2019, Nanoscale Research Letters.

[341]  Michael Unser,et al.  Convolutional Neural Networks for Inverse Problems in Imaging: A Review , 2017, IEEE Signal Processing Magazine.

[342]  Allan Pinkus,et al.  Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.

[343]  Yuri Niyazov,et al.  Open Access Meets Discoverability: Citations to Articles Posted to Academia.edu , 2016, PloS one.

[344]  Abhishek Das,et al.  Impact of Data Normalization on Deep Neural Network for Time Series Forecasting , 2018, ArXiv.

[345]  Beomsu Kim,et al.  Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[346]  Boris Ginsburg,et al.  Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification , 2017, ArXiv.

[347]  Pavlo O. Dral,et al.  Quantum Chemistry in the Age of Machine Learning. , 2020, The journal of physical chemistry letters.

[348]  Javier Rodrigo,et al.  Improved Bayesian image denoising based on wavelets with applications to electron microscopy , 2006, Pattern Recognit..

[349]  Yann Dauphin,et al.  MetaInit: Initializing learning by learning to initialize , 2019, NeurIPS.

[350]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[351]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[352]  Frans Coenen,et al.  FCNN: Fourier Convolutional Neural Networks , 2017, ECML/PKDD.

[353]  Shiyu Chang,et al.  AutoGAN: Neural Architecture Search for Generative Adversarial Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[354]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[355]  Kaile Su,et al.  Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano's Continuous Note Recognition , 2017, J. Robotics.

[356]  Pierre Moulin,et al.  A New Variational Method for Deep Supervised Semantic Image Hashing , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[357]  Jeppe Rich,et al.  How to generate micro-agents? A deep generative modeling approach to population synthesis , 2019, Transportation Research Part C: Emerging Technologies.

[358]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[359]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[360]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[361]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[362]  Nima Tajbakhsh,et al.  Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..

[363]  Rui Shu AC-GAN Learns a Biased Distribution , 2017 .

[364]  Ondrej Dyck,et al.  Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images , 2018, npj Computational Materials.

[365]  Jun Tani,et al.  The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory , 2013, Front. Neurorobot..

[366]  Quanzheng Li,et al.  Speckle Noise Removal in Ultrasound Images Using a Deep Convolutional Neural Network and a Specially Designed Loss Function , 2019, MMMI@MICCAI.

[367]  Aastha Dutta Fourier Transform Infrared Spectroscopy , 2017 .

[368]  Digital electron diffraction – seeing the whole picture , 2012, Acta crystallographica. Section A, Foundations of crystallography.

[369]  Stefan C. Kremer,et al.  Recurrent Neural Networks , 2013, Handbook on Neural Information Processing.

[370]  Fei Su,et al.  Deep learning analysis on microscopic imaging in materials science , 2020 .

[371]  D. Van Dyck,et al.  Object wavefunction reconstruction in high resolution electron microscopy , 1994, Proceedings of 1st International Conference on Image Processing.

[372]  Vina Ayumi,et al.  Optimization of convolutional neural network using microcanonical annealing algorithm , 2016, 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[373]  Gerald Tesauro,et al.  Programming backgammon using self-teaching neural nets , 2002, Artif. Intell..

[374]  Francesco Visin,et al.  A guide to convolution arithmetic for deep learning , 2016, ArXiv.

[375]  V. Maxim,et al.  Fast electron tomography: Applications to beam sensitive samples and in situ TEM or operando environmental TEM studies , 2019, Materials Characterization.

[376]  Liang Xue,et al.  TEM bright field imaging of thick specimens: nodes in Thon ring patterns. , 2020, Ultramicroscopy.

[377]  F. Grunthaner,et al.  Auger Electron Spectroscopy , 1976, 14th International Reliability Physics Symposium.

[378]  Mahima Vuppuluri,et al.  Survey on Generative Adversarial Networks , 2017 .

[379]  R. L. Wal Soot precursor carbonization: Visualization using LIF and LII and comparison using bright and dark field TEM , 1998 .

[380]  Patricia M. Johnson,et al.  Improving the Speed of MRI with Artificial Intelligence , 2020, Seminars in Musculoskeletal Radiology.

[381]  Renjie Liao,et al.  Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes , 2016, ICLR.

[382]  A. C. Furnival,et al.  Open Access to scholarly communications: Advantages, policy and advocacy , 2010 .

[383]  Guoyin Wang,et al.  Generative Adversarial Network Training is a Continual Learning Problem , 2018, ArXiv.

[384]  Jan Kautz,et al.  High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[385]  Richard Granger,et al.  Toward the quantification of cognition , 2020, ArXiv.

[386]  Stephen Jesse,et al.  Deep Learning as a Tool for Image Denoising and Drift Correction , 2019, Microscopy and Microanalysis.

[387]  David Rolnick,et al.  Deep ReLU Networks Have Surprisingly Few Activation Patterns , 2019, NeurIPS.

[388]  Peter Stone,et al.  Agents teaching agents: a survey on inter-agent transfer learning , 2019, Autonomous Agents and Multi-Agent Systems.

[389]  Loic A. Royer,et al.  Content-aware image restoration: pushing the limits of fluorescence microscopy , 2018, Nature Methods.

[390]  Prakhar Gupta,et al.  Learning Word Vectors for 157 Languages , 2018, LREC.

[391]  Gibson Adam,et al.  Deeplearning4j: Distributed, open-source deep learning for Java and Scala on Hadoop and Spark , 2016 .

[392]  Steven C. H. Hoi,et al.  Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[393]  Marcel van Gerven,et al.  Explainable Deep Learning: A Field Guide for the Uninitiated , 2020, J. Artif. Intell. Res..

[394]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[395]  Lorenzo Beretta,et al.  Nearest neighbor imputation algorithms: a critical evaluation , 2016, BMC Medical Informatics and Decision Making.

[396]  Geoffrey E. Hinton,et al.  Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.

[397]  Shirin Tavara,et al.  Parallel Computing of Support Vector Machines , 2019, ACM Comput. Surv..

[398]  Chen-Yu Wei,et al.  Online Reinforcement Learning in Stochastic Games , 2017, NIPS.

[399]  Jürgen Schmidhuber,et al.  LSTM recurrent networks learn simple context-free and context-sensitive languages , 2001, IEEE Trans. Neural Networks.

[400]  F. Clarke,et al.  Helmholtz Reciprocity: its validity and application to reflectometry , 1985 .

[401]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[402]  Preslav Nakov,et al.  Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications , 2019, TACL.

[403]  MAXIMUM ENTROPY RESTORATION OF ELECTRON MICROSCOPE IMAGES WITH A RANDOM-SPATIAL-DISTRIBUTION CONSTRAINT , 1997 .

[404]  Steven C. H. Hoi,et al.  Online Learning: A Comprehensive Survey , 2018, Neurocomputing.

[405]  Sotirios A. Tsaftaris,et al.  Deep Multi-Class Segmentation Without Ground-Truth Labels , 2018 .

[406]  Yuhui Zheng,et al.  Recent Progress on Generative Adversarial Networks (GANs): A Survey , 2019, IEEE Access.

[407]  Sepp Hochreiter,et al.  Self-Normalizing Neural Networks , 2017, NIPS.

[408]  David Kaeli,et al.  Heterogeneous Computing with OpenCL , 2011 .

[409]  Yuriy Kochura,et al.  Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models , 2019, Deep Learning: Concepts and Architectures.

[410]  L J Allen,et al.  Direct exit-wave reconstruction from a single defocused image. , 2011, Ultramicroscopy.

[411]  Jane X. Wang,et al.  Reinforcement Learning, Fast and Slow , 2019, Trends in Cognitive Sciences.

[412]  Zhiqiang Tian,et al.  Exploiting confident information for weakly supervised prostate segmentation based on image-level labels , 2020, Medical Imaging: Image-Guided Procedures.

[413]  Hanno Scharr,et al.  Principles of Filter Design , 1999 .

[414]  Amit Kumar Mondal A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions , 2020, ArXiv.

[415]  Ping Luo,et al.  Differentiable Learning-to-Normalize via Switchable Normalization , 2018, ICLR.

[416]  Kwang Min Yu,et al.  Bounding the expected run-time of nonconvex optimization with early stopping , 2020, UAI.

[417]  Harald C. Gall,et al.  Software Engineering for Machine Learning: A Case Study , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).

[418]  D. Peckys,et al.  The Effect of Electron Beam Irradiation in Environmental Scanning Transmission Electron Microscopy of Whole Cells in Liquid , 2016, Microscopy and Microanalysis.

[419]  Sebastian Nowozin,et al.  DeepCoder: Learning to Write Programs , 2016, ICLR.

[420]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[421]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[422]  S. Pennycook The impact of STEM aberration correction on materials science. , 2017, Ultramicroscopy.

[423]  Hao Chen,et al.  Deep Learning for Source Code Modeling and Generation , 2020, ACM Comput. Surv..

[424]  Carlo Luschi,et al.  Revisiting Small Batch Training for Deep Neural Networks , 2018, ArXiv.

[425]  Sébastien Le Digabel,et al.  HyperNOMAD , 2019, ACM Trans. Math. Softw..

[426]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[427]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[428]  Artur Strzelecki,et al.  The impact of Google on discovering scholarly information: managing STM publishers’ visibility in Google , 2020 .

[429]  Tejs Vegge,et al.  Genetic algorithms for computational materials discovery accelerated by machine learning , 2019, npj Computational Materials.

[430]  Alberto Cano,et al.  A survey on graphic processing unit computing for large‐scale data mining , 2018, WIREs Data Mining Knowl. Discov..

[431]  Shan Li,et al.  Deep Job Understanding at LinkedIn , 2020, SIGIR.

[432]  J. Verbeeck,et al.  Fundamentals of Focal Series Inline Electron Holography , 2016 .

[433]  John Sum,et al.  A Limitation of Gradient Descent Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[434]  Claire Creaser,et al.  The influence of journal publisher characteristics on open access policy trends , 2018, Scientometrics.

[435]  Herbert Jaeger,et al.  Echo state network , 2007, Scholarpedia.

[436]  Andrew Zisserman,et al.  Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.

[437]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[438]  Neil A. Ernst,et al.  Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process? , 2019, 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[439]  J. Zou,et al.  Electron Tomography: A Unique Tool Solving Intricate Hollow Nanostructures , 2018, Advanced materials.

[440]  Jascha Sohl-Dickstein,et al.  Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks , 2018, ICML.

[441]  Teng Wang,et al.  An Overview of FPGA Based Deep Learning Accelerators: Challenges and Opportunities , 2019, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[442]  Christian Herzog,et al.  Dimensions: Building Context for Search and Evaluation , 2018, Front. Res. Metr. Anal..

[443]  J M Carazo,et al.  MicrographCleaner: a python package for cryo-EM micrograph cleaning using deep learning. , 2020, Journal of structural biology.

[444]  T. Lamb,et al.  Why rods and cones? , 2016, Eye.

[445]  Eric M. S. P. Veith,et al.  Explainable Reinforcement Learning: A Survey , 2020, CD-MAKE.

[446]  Leslie Lamport,et al.  L A T E X (2nd ed.): a document preparation system: user's guide and reference manual , 1994 .

[447]  Alán Aspuru-Guzik,et al.  Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space , 2020, ICLR.

[448]  Shaohuai Shi,et al.  Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training , 2019, 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID).

[449]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[450]  Paolo Favaro,et al.  On Stabilizing Generative Adversarial Training With Noise , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[451]  Jeffrey M. Ede Deep Learning Supersampled Scanning Transmission Electron Microscopy , 2019, ArXiv.

[452]  F. Krumeich Properties of Electrons, their Interactions with Matter and Applications in Electron Microscopy , 2015 .

[453]  Feng Jiang,et al.  An End-to-End Compression Framework Based on Convolutional Neural Networks , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[454]  Dan Fu,et al.  Denoising of stimulated Raman scattering microscopy images via deep learning. , 2019, Biomedical optics express.

[455]  Andrius Merkys,et al.  Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database , 2018, Journal of Cheminformatics.

[456]  세르게이 이오페,et al.  Batch normalization layers , 2016 .

[457]  Aaron Klein,et al.  Efficient and Robust Automated Machine Learning , 2015, NIPS.

[458]  Jong Chul Ye,et al.  Understanding Geometry of Encoder-Decoder CNNs , 2019, ICML.

[459]  Kilian Q. Weinberger,et al.  Convolutional Networks with Dense Connectivity , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[460]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[461]  L. Carin,et al.  The potential for Bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images. , 2014, Microscopy.

[462]  F. J. Humphreys Review Grain and subgrain characterisation by electron backscatter diffraction , 2001 .

[463]  R. Egerton Radiation damage to organic and inorganic specimens in the TEM. , 2019, Micron.

[464]  Aniruddha Parvat,et al.  A survey of deep-learning frameworks , 2017, 2017 International Conference on Inventive Systems and Control (ICISC).

[465]  Neel Joshi,et al.  A Comprehensive Survey of Services Provided by Prevalent Cloud Computing Environments , 2019 .

[466]  Piero Molino,et al.  Ludwig: a type-based declarative deep learning toolbox , 2019, ArXiv.

[467]  D. Krahl,et al.  Performance of a low-noise CCD camera adapted to a transmission electron microscope , 1992 .

[468]  Marcin Andrychowicz,et al.  Learning to learn by gradient descent by gradient descent , 2016, NIPS.

[469]  Aderemi A. Atayero,et al.  Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend , 2019, Neurocomputing.

[470]  Jerry Ma,et al.  Quasi-hyperbolic momentum and Adam for deep learning , 2018, ICLR.

[471]  Jian Zhou,et al.  On Performance of Peer Review for Academic Journals: Analysis Based on Distributed Parallel System , 2019, IEEE Access.

[472]  Li Yang,et al.  On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice , 2020, Neurocomputing.

[473]  Joseph Paul Cohen,et al.  Deep semantic segmentation of natural and medical images: a review , 2019, Artificial Intelligence Review.

[474]  J. Güémez,et al.  The principle of relativity and the de Broglie relation , 2016 .

[475]  Risto Miikkulainen,et al.  Discovering Parametric Activation Functions , 2020, Neural Networks.

[476]  Fei Wang,et al.  Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background , 2019, Energies.

[477]  Nantheera Anantrasirichai,et al.  Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography , 2014, Comput. Medical Imaging Graph..

[478]  R. B. Deshmukh,et al.  A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications , 2018, IEEE Access.

[479]  Fabio Viola,et al.  The Kinetics Human Action Video Dataset , 2017, ArXiv.

[480]  D. Muller,et al.  Electron tomography for functional nanomaterials , 2020, MRS Bulletin.

[481]  Martin A. Riedmiller,et al.  Improving Deep Neural Networks with Probabilistic Maxout Units , 2013, ICLR.

[482]  Leon A. Gatys,et al.  A Neural Algorithm of Artistic Style , 2015, ArXiv.

[483]  Shifeng Zhang,et al.  DARTS+: Improved Differentiable Architecture Search with Early Stopping , 2019, ArXiv.

[484]  Yibo Zhang,et al.  Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery , 2018, Optica.

[485]  Stefanie Jegelka,et al.  ResNet with one-neuron hidden layers is a Universal Approximator , 2018, NeurIPS.

[486]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[487]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[488]  Matthew B Hoy Rise of the Rxivs: How Preprint Servers are Changing the Publishing Process , 2020, Medical reference services quarterly.

[489]  Samet Demir,et al.  Neural Academic Paper Generation , 2019, ArXiv.

[490]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[491]  Kristof T. Schütt,et al.  Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions , 2019, Nature Communications.

[492]  Brendan J. Frey,et al.  k-Sparse Autoencoders , 2013, ICLR.

[493]  Sunil Agrawal,et al.  Image denoising review: From classical to state-of-the-art approaches , 2020, Inf. Fusion.

[494]  Vinay P. Namboodiri,et al.  U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[495]  Jun Zhao,et al.  Deep Semantic Hashing with Multi-Adversarial Training , 2018, CIKM.

[496]  S. Bargeri,et al.  Publish or perish: Reporting Characteristics of Peer-reviewed publications, pre-prints and registered studies on the COVID-19 pandemic , 2020, medRxiv.

[497]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[498]  Hwee Tou Ng,et al.  A Neural Approach to Automated Essay Scoring , 2016, EMNLP.

[499]  Steven R. Young,et al.  Vertex reconstruction of neutrino interactions using deep learning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[500]  Oscar Déniz-Suárez,et al.  Robustness to adversarial examples can be improved with overfitting , 2020, Int. J. Mach. Learn. Cybern..

[501]  Helina Marshall,et al.  In praise of preprints. , 2019, International journal of systematic and evolutionary microbiology.

[502]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[503]  Paul Barham,et al.  Machine Learning Systems are Stuck in a Rut , 2019, HotOS.

[504]  Yilong Yin,et al.  A brief survey of visual saliency detection , 2020, Multimedia Tools and Applications.

[505]  Zhixiang Wu,et al.  A Survey of Generative Adversarial Networks Based on Encoder-Decoder Model , 2020 .

[506]  Vitaly Shmatikov,et al.  You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion , 2020, USENIX Security Symposium.

[507]  Jarmo Takala,et al.  Hardware architectures for the fast Fourier transform , 2018, Handbook of Signal Processing Systems.

[508]  Shadrokh Samavi,et al.  Modeling Neural Architecture Search Methods for Deep Networks , 2019, ArXiv.

[509]  Zhiwei Huang,et al.  Deep Learning in the Field of Art , 2019 .

[510]  Xin Yuan,et al.  Deep learning for video compressive sensing , 2020, APL Photonics.

[511]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

[512]  H. Atwater,et al.  Reflection electron energy loss spectroscopy during initial stages of Ge growth on Si by molecular beam epitaxy , 1991 .

[513]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[514]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[515]  Krishna P. Gummadi,et al.  Search bias quantification: investigating political bias in social media and web search , 2018, Information Retrieval Journal.

[516]  Hao Li,et al.  Visualizing the Loss Landscape of Neural Nets , 2017, NeurIPS.

[517]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[518]  Ghassen Jerfel,et al.  AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles , 2019, ArXiv.

[519]  Tao Xue,et al.  Unbiased Auxiliary Classifier GANs with MINE , 2020, ArXiv.

[520]  M. Nastasi,et al.  Radiation damage in nanostructured materials , 2018, Progress in Materials Science.

[521]  Methodologies for Successful Segmentation of HRTEM Images via Neural Network , 2020, ArXiv.

[522]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..

[523]  Akira Tonomura,et al.  Applications of electron holography , 1987 .

[524]  R Henderson,et al.  Direct Electron Detectors. , 2016, Methods in enzymology.

[525]  Stefano V. Albrecht,et al.  Stabilizing Generative Adversarial Network Training: A Survey , 2019, ArXiv.

[526]  Z. Ding,et al.  Bohmian trajectory-bloch wave approach to dynamical simulation of electron diffraction in crystal , 2018, New Journal of Physics.

[527]  Stewart A. Koppell,et al.  Multi-pass transmission electron microscopy , 2016, Scientific Reports.

[528]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[529]  J. Legoux,et al.  Dynamic recrystallization in the particle/particle interfacial region of cold-sprayed nickel coating: Electron backscatter diffraction characterization , 2009 .

[530]  Caslav Livada,et al.  Compression parameters tuning for automatic image optimization in web applications , 2016, 2016 International Symposium ELMAR.

[531]  L. Schultz,et al.  Atomic surface diffusion on Pt nanoparticles quantified by high-resolution transmission electron microscopy. , 2014, Micron.

[532]  Sergey Ioffe,et al.  Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models , 2017, NIPS.

[533]  Xavier Lladó,et al.  Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review , 2017, Artif. Intell. Medicine.

[534]  L. F. Abbott,et al.  Random Walk Initialization for Training Very Deep Feedforward Networks , 2014, 1412.6558.

[535]  Y. Alqaheem,et al.  Microscopy and Spectroscopy Techniques for Characterization of Polymeric Membranes , 2020, Membranes.

[536]  Kazunari Yoshizawa,et al.  Macroscopic Polarization Change via Electron Transfer in a Valence Tautomeric Cobalt Complex , 2020, Nature Communications.

[537]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[538]  Guillaume Lample,et al.  Playing FPS Games with Deep Reinforcement Learning , 2016, AAAI.

[539]  Yoshua Bengio,et al.  Revisiting Fundamentals of Experience Replay , 2020, ICML.

[540]  Earl J. Kirkland,et al.  Advanced Computing in Electron Microscopy , 1998 .

[541]  Tomas E. Ward,et al.  Generative Adversarial Networks: A Survey and Taxonomy , 2019, ArXiv.

[542]  Ghadah Alamer,et al.  Open Source Software Hosting Platforms: A Collaborative Perspective's Review , 2017, J. Softw..

[543]  W. Joines,et al.  Tunable and Anisotropic Dual-Band Metamaterial Absorber Using Elliptical Graphene-Black Phosphorus Pairs , 2019, Nanoscale Research Letters.

[544]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[545]  Keziban Orbay,et al.  Building Journal Impact Factor Quartile into the Assessment of Academic Performance: A Case Study , 2020, ArXiv.

[546]  Alex Sherstinsky,et al.  Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network , 2018, Physica D: Nonlinear Phenomena.

[547]  H. S. Kushwaha,et al.  De-noising Filters for TEM (Transmission Electron Microscopy) Image of Nanomaterials , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[548]  Smita Krishnaswamy,et al.  TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[549]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[550]  Gang Yin,et al.  Studying in the ‘Bazaar’: An Exploratory Study of Crowdsourced Learning in GitHub , 2019, IEEE Access.

[551]  Saulius Gražulis,et al.  Computing stoichiometric molecular composition from crystal structures , 2015, Journal of applied crystallography.

[552]  Michael Zibulevsky,et al.  Block-based compressed sensing of images via deep learning , 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).

[553]  Gabriel Kreiman,et al.  Gradient-free activation maximization for identifying effective stimuli , 2019, ArXiv.

[554]  Gulshan Kumar,et al.  A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning , 2019, Archives of Computational Methods in Engineering.

[555]  Allan Pinkus,et al.  Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.

[556]  Fu,et al.  Correction of aberrations of an electron microscope by means of electron holography. , 1991, Physical review letters.

[557]  J. Alison Noble,et al.  Multi-task CNN for Structural Semantic Segmentation in 3D Fetal Brain Ultrasound , 2019, MIUA.

[558]  Martha White,et al.  Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study , 2019, ArXiv.

[559]  Lovedeep Gondara,et al.  Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[560]  Jaakko Lehtinen,et al.  Noise2Noise: Learning Image Restoration without Clean Data , 2018, ICML.

[561]  Jin Liu,et al.  Applications of deep learning to MRI images: A survey , 2018, Big Data Min. Anal..

[562]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[563]  Samsul Ariffin Abdul Karim,et al.  Rational bicubic Ball for image interpolation , 2019 .

[564]  Guillermo Sapiro,et al.  Evaluation of denoising algorithms for biological electron tomography. , 2008, Journal of structural biology.

[565]  Xiaofeng Xu,et al.  In Situ Atom Scale Visualization of Domain Wall Dynamics in VO2 Insulator-Metal Phase Transition , 2014, Scientific Reports.

[566]  Alasdair McAndrew,et al.  A Computational Introduction to Digital Image Processing , 2015 .

[567]  A. Schmid,et al.  A study of chiral magnetic stripe domains within an in-plane virtual magnetic field using SPLEEM , 2017 .

[568]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[569]  S. An,et al.  Synthesis and degradation kinetics of TiO2/GO composites with highly efficient activity for adsorption and photocatalytic degradation of MB , 2019, Scientific Reports.

[570]  Van Lam,et al.  Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. , 2017, Optics express.

[571]  Dimitry Tegunov,et al.  Real-time cryo–EM data pre-processing with Warp , 2019, Nature Methods.

[572]  Jürgen Schmidhuber,et al.  Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.

[573]  R. Egerton,et al.  Mechanisms of radiation damage in beam‐sensitive specimens, for TEM accelerating voltages between 10 and 300 kV , 2012, Microscopy research and technique.

[574]  Tim Salimans,et al.  Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.

[575]  Paulo Cortez,et al.  A deep learning classifier for sentence classification in biomedical and computer science abstracts , 2019, Neural Computing and Applications.

[576]  Michael I. Jordan Serial Order: A Parallel Distributed Processing Approach , 1997 .

[577]  Yu-Bin Yang,et al.  Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections , 2016, NIPS.

[578]  Marcin Andrychowicz,et al.  Parameter Space Noise for Exploration , 2017, ICLR.

[579]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[580]  Haoxiang Zhang,et al.  Reading Answers on Stack Overflow: Not Enough! , 2021, IEEE Transactions on Software Engineering.

[581]  B. Miller,et al.  Real-Time Data Processing using Python in DigitalMicrograph , 2019, Microscopy and Microanalysis.

[582]  Quoc V. Le,et al.  Adversarial Examples Improve Image Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[583]  Matthew W. Hoffman,et al.  Distributed Distributional Deterministic Policy Gradients , 2018, ICLR.

[584]  Jutta Haider,et al.  Invisible Search and Online Search Engines , 2019 .

[585]  Werner Dubitzky,et al.  A Practical Approach to Microarray Data Analysis , 2003, Springer US.

[586]  Dimitar Filev,et al.  Autonomous Highway Driving using Deep Reinforcement Learning , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[587]  Max Tegmark,et al.  Toward an artificial intelligence physicist for unsupervised learning. , 2019, Physical review. E.

[588]  K Henrick,et al.  EMDep: a web-based system for the deposition and validation of high-resolution electron microscopy macromolecular structural information. , 2003, Journal of structural biology.

[589]  S. Haigh,et al.  Recording low and high spatial frequencies in exit wave reconstructions. , 2013, Ultramicroscopy.

[590]  U. Kaiser,et al.  Electron radiation damage mechanisms in 2D MoSe2 , 2017 .

[591]  John D. Westbrook,et al.  EMDataBank unified data resource for 3DEM , 2013, Nucleic Acids Res..

[592]  A. J. D’Alfonso,et al.  Quantitative atomic resolution elemental mapping via absolute-scale energy dispersive X-ray spectroscopy. , 2016, Ultramicroscopy.

[593]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[594]  B. P. Zhang,et al.  Estimation of the Lipschitz constant of a function , 1996, J. Glob. Optim..

[595]  R. Beanland,et al.  Structure refinement from 'digital' large angle convergent beam electron diffraction patterns. , 2018, Ultramicroscopy.

[596]  Eesha Goel,et al.  Random Forest: A Review , 2017 .

[597]  J. Tukey,et al.  An Algorithm for the Machine Calculation of , 2016 .

[598]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[599]  Shreyas Fadnavis,et al.  Image Interpolation Techniques in Digital Image Processing: An Overview , 2014 .

[600]  Daochen Zha,et al.  Experience Replay Optimization , 2019, IJCAI.

[601]  Richard S. Sutton,et al.  A Deeper Look at Experience Replay , 2017, ArXiv.

[602]  Quoc V. Le,et al.  DropBlock: A regularization method for convolutional networks , 2018, NeurIPS.

[603]  D. Butt,et al.  In situ transmission electron microscopy of electron-beam induced damage process in nuclear grade graphite , 2011 .

[604]  Lei Lei,et al.  Should highly cited items be excluded in impact factor calculation? The effect of review articles on journal impact factor , 2020, Scientometrics.

[605]  K. Ishizuka,et al.  A practical approach for STEM image simulation based on the FFT multislice method. , 2002, Ultramicroscopy.

[606]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[607]  K. Claffy,et al.  New Phenomena in Large-Scale Internet Traffic , 2019, ArXiv.

[608]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[609]  Isabelle Guyon,et al.  A Scaling Law for the Validation-Set Training-Set Size Ratio , 1997 .

[610]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[611]  Mouna Afif,et al.  Computer vision algorithms acceleration using graphic processors NVIDIA CUDA , 2020, Cluster Computing.

[612]  Wolfgang Dahmen,et al.  Poisson noise removal from high-resolution STEM images based on periodic block matching , 2015, Advanced Structural and Chemical Imaging.

[613]  Yu Wang,et al.  [DL] A Survey of FPGA-based Neural Network Inference Accelerators , 2019, ACM Trans. Reconfigurable Technol. Syst..

[614]  Ramesh Raskar,et al.  Accelerating Neural Architecture Search using Performance Prediction , 2017, ICLR.

[615]  Demis Hassabis,et al.  Improved protein structure prediction using potentials from deep learning , 2020, Nature.

[616]  Pan He,et al.  Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[617]  Grant J Jensen,et al.  ETDB-Caltech: A blockchain-based distributed public database for electron tomography , 2018, bioRxiv.

[618]  J. M. Drake,et al.  Special Issue: Dynamics of Molecular Systems , 1990 .

[619]  Diederik P. Kingma,et al.  An Introduction to Variational Autoencoders , 2019, Found. Trends Mach. Learn..

[620]  Lars Schmidt-Thieme,et al.  Learning Surrogate Losses , 2019, ArXiv.

[621]  A. Krizhevsky Convolutional Deep Belief Networks on CIFAR-10 , 2010 .

[622]  Sepp Hochreiter,et al.  Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation , 2019, Explainable AI.

[623]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[624]  C. Beeli,et al.  An experiment on electron wave-particle duality including a Planck constant measurement , 1998 .

[625]  Suvrit Sra,et al.  Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity , 2019, ICLR.

[626]  Fuat Bilgili,et al.  Semantic segmentation of the multiform proximal femur and femoral head bones with the deep convolutional neural networks in low quality MRI sections acquired in different MRI protocols , 2020, Comput. Medical Imaging Graph..

[627]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[628]  Jannik C. Meyer,et al.  Experimental analysis of charge redistribution due to chemical bonding by high-resolution transmission electron microscopy. , 2011, Nature materials.

[629]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[630]  Zoubeida Messali,et al.  Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images , 2015, Entropy.

[631]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[632]  M. Hutson Artificial intelligence faces reproducibility crisis. , 2018, Science.

[633]  M F Crommie,et al.  Direct imaging of lattice atoms and topological defects in graphene membranes. , 2008, Nano letters.

[634]  Qingmin Liao,et al.  XSepConv: Extremely Separated Convolution , 2020, ArXiv.

[635]  Dhiraj D. Kalamkar,et al.  K-TanH: Hardware Efficient Activations For Deep Learning , 2019, ArXiv.

[636]  Jun Tani,et al.  How Hierarchical Control Self-organizes in Artificial Adaptive Systems , 2005, Adapt. Behav..

[637]  Zhiyuan Zhang,et al.  Understanding and Improving Layer Normalization , 2019, NeurIPS.

[638]  David Rolnick,et al.  How to Start Training: The Effect of Initialization and Architecture , 2018, NeurIPS.

[639]  Ernesto G. Rodríguez,et al.  Preprints and preprint servers as academic communication tools , 2019 .

[640]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[641]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[642]  Fathi M. Salem,et al.  Gate-variants of Gated Recurrent Unit (GRU) neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[643]  P. Luksch,et al.  New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. , 2002, Acta crystallographica. Section B, Structural science.

[644]  Michael Garland,et al.  AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks , 2017, ArXiv.

[645]  Dan Zhou,et al.  Sample tilt effects on atom column position determination in ABF-STEM imaging. , 2016, Ultramicroscopy.

[646]  Sebastian Nowozin,et al.  Which Training Methods for GANs do actually Converge? , 2018, ICML.

[647]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[648]  Zhao Zhang,et al.  Deep Learning-Based Point-Scanning Super-Resolution Imaging , 2019, Nature Methods.

[649]  Lewys Jones,et al.  Identifying and Correcting Scan Noise and Drift in the Scanning Transmission Electron Microscope , 2013, Microscopy and Microanalysis.

[650]  Semantic Hashing with Variational Autoencoders , 2022 .

[651]  Thierry Blu,et al.  A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding , 2007, IEEE Transactions on Image Processing.

[652]  Seyed-Mohsen Moosavi-Dezfooli,et al.  Adaptive Quantization for Deep Neural Network , 2017, AAAI.

[653]  Jesse Johnson,et al.  Deep, Skinny Neural Networks are not Universal Approximators , 2018, ICLR.

[654]  Renu Sharma,et al.  Determination of atomic positions from time resolved high resolution transmission electron microscopy images. , 2018, Ultramicroscopy.

[655]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[656]  Dong-Qing Zhang Image Recognition Using Scale Recurrent Neural Networks , 2018, ArXiv.

[657]  Ladislav Hluchý,et al.  Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey , 2019, Artificial Intelligence Review.

[658]  Lennart Martens,et al.  Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows. , 2020, Mass spectrometry reviews.

[659]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[660]  Colin Ophus,et al.  Nanomaterial datasets to advance tomography in scanning transmission electron microscopy , 2016, Scientific Data.

[661]  H. S. Prasantha,et al.  Compressed Sensing for Image Compression: Survey of Algorithms , 2019, Emerging Research in Computing, Information, Communication and Applications.

[662]  Ilya Sutskever,et al.  Jukebox: A Generative Model for Music , 2020, ArXiv.

[663]  P. Nellist Introduction to Scanning Transmission Electron Microscopy , 2002 .

[664]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[665]  O'Keefe,et al.  ADVANCES IN HIGH-RESOLUTION IMAGE SIMULATION , 1988 .

[666]  Weiping Ding,et al.  Automatic Construction of Multi-layer Perceptron Network from Streaming Examples , 2019, CIKM.

[667]  J. Verbeeck,et al.  Progress and new advances in simulating electron microscopy datasets using MULTEM. , 2016, Ultramicroscopy.

[668]  Walter Dehnen,et al.  A Hierarchical O(N) Force Calculation Algorithm , 2002 .

[669]  Kirthevasan Kandasamy,et al.  Neural Architecture Search with Bayesian Optimisation and Optimal Transport , 2018, NeurIPS.

[670]  Tomaso A. Poggio,et al.  Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex , 2016, ArXiv.

[671]  Xiaoqing Ding,et al.  Beyond weights adaptation: a new neuron model with trainable activation function and its supervised learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[672]  Hong Seok Lim,et al.  Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model , 2019, Healthcare informatics research.

[673]  Zhi-Qin John Xu,et al.  Understanding training and generalization in deep learning by Fourier analysis , 2018, ArXiv.

[674]  Patrick Kidger,et al.  Universal Approximation with Deep Narrow Networks , 2019, COLT 2019.

[675]  Peter Alfeld,et al.  A trivariate clough-tocher scheme for tetrahedral data , 1984, Comput. Aided Geom. Des..

[676]  Samy Bengio,et al.  Torch: a modular machine learning software library , 2002 .

[677]  Carole-Jean Wu,et al.  Machine Learning at Facebook: Understanding Inference at the Edge , 2019, 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[678]  Gongjian Wen,et al.  Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder , 2018, Comput. Vis. Image Underst..

[679]  Quanquan Gu,et al.  Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks , 2019, AAAI.

[680]  Li Fei-Fei,et al.  Progressive Neural Architecture Search , 2017, ECCV.

[681]  Alán Aspuru-Guzik,et al.  Deep learning enables rapid identification of potent DDR1 kinase inhibitors , 2019, Nature Biotechnology.

[682]  Aydogan Ozcan,et al.  Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy , 2020, Proceedings of the IEEE.

[683]  S. Arumuga Perumal,et al.  Image De-noising using Discrete Wavelet transform , 2008 .

[684]  Yong Yu,et al.  A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures , 2019, Neural Computation.

[685]  Quoc V. Le,et al.  Searching for Activation Functions , 2018, arXiv.

[686]  Sebastian Nowozin,et al.  Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.

[687]  Trevor Darrell,et al.  Regularization Matters in Policy Optimization , 2019, ArXiv.

[688]  Lutz Bornmann,et al.  Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references , 2014, J. Assoc. Inf. Sci. Technol..

[689]  M. Kosinski,et al.  Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images , 2018, Journal of personality and social psychology.

[690]  P. Ginsparg ArXiv at 20 , 2011, Nature.

[691]  Benjamin Doerr,et al.  Fast genetic algorithms , 2017, GECCO.

[692]  Zhanxing Zhu,et al.  Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes , 2017, ArXiv.

[693]  Maithra Raghu,et al.  A Survey of Deep Learning for Scientific Discovery , 2020, ArXiv.

[694]  E. Cambria,et al.  Deep Learning--based Text Classification , 2020, ACM Comput. Surv..

[695]  Dimitrios I. Fotiadis,et al.  Medical data quality assessment: On the development of an automated framework for medical data curation , 2019, Comput. Biol. Medicine.

[696]  Cornelius Puschmann,et al.  Beyond the Bubble: Assessing the Diversity of Political Search Results , 2018, Digital Journalism.

[697]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[698]  R. Egerton Control of radiation damage in the TEM. , 2013, Ultramicroscopy.

[699]  Weiyao Lin,et al.  Network Decoupling: From Regular to Depthwise Separable Convolutions , 2018, BMVC.

[700]  Anastasios Kyrillidis,et al.  Decaying momentum helps neural network training , 2019, ArXiv.

[701]  Philipp Hennig,et al.  Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers , 2020, ICML.

[702]  Sungzoon Cho,et al.  Variational Autoencoder based Anomaly Detection using Reconstruction Probability , 2015 .

[703]  Danica Kragic,et al.  The effect of Target Normalization and Momentum on Dying ReLU , 2020, ArXiv.

[704]  Yoshua Bengio,et al.  Depth with Nonlinearity Creates No Bad Local Minima in ResNets , 2019, Neural Networks.

[705]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[706]  Victor Talpaert,et al.  Exploring applications of deep reinforcement learning for real-world autonomous driving systems , 2019, VISIGRAPP.

[707]  A. Romano An Introduction to Special Relativity , 2012 .

[708]  E. S. Gedraite,et al.  Investigation on the effect of a Gaussian Blur in image filtering and segmentation , 2011, Proceedings ELMAR-2011.

[709]  Peter Moeck,et al.  Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration , 2011, Nucleic Acids Res..

[710]  Ting-Chun Wang,et al.  Image Inpainting for Irregular Holes Using Partial Convolutions , 2018, ECCV.

[711]  Yuichi Yoshida,et al.  Spectral Norm Regularization for Improving the Generalizability of Deep Learning , 2017, ArXiv.

[712]  Yi Sun,et al.  Limited Gradient Descent: Learning With Noisy Labels , 2018, IEEE Access.

[713]  Musatafa Abbas Abbood Albadr,et al.  Extreme learning machine: A review , 2017 .

[714]  Lord Rayleigh F.R.S. LVI. Investigations in optics, with special reference to the spectroscope , 1879 .

[715]  Daniel Cremers,et al.  Regularization for Deep Learning: A Taxonomy , 2017, ArXiv.

[716]  Quoc V. Le,et al.  Adding Gradient Noise Improves Learning for Very Deep Networks , 2015, ArXiv.

[717]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[718]  Wangmeng Zuo,et al.  Toward Convolutional Blind Denoising of Real Photographs , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[719]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[720]  Zhao Peng,et al.  Multilayer Perceptron Algebra , 2017, ArXiv.

[721]  Jeffrey M. Ede Warwick Electron Microscopy Datasets , 2020, ArXiv.

[722]  P. Hansma,et al.  Atomic force microscopy , 1990, Nature.

[723]  Matthias Hagen,et al.  The Effect of Content-Equivalent Near-Duplicates on the Evaluation of Search Engines , 2020, ECIR.

[724]  Leslie J. Allen,et al.  Direct retrieval of a complex wave from its diffraction pattern , 2008 .

[725]  Tamás Linder,et al.  Asymptotic Optimality of Finite Model Approximations for Partially Observed Markov Decision Processes With Discounted Cost , 2017, IEEE Transactions on Automatic Control.

[726]  Geoffrey Zweig,et al.  Achieving Human Parity in Conversational Speech Recognition , 2016, ArXiv.

[727]  Pierre Alliez,et al.  StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[728]  Farid García,et al.  A comprehensive survey on support vector machine classification: Applications, challenges and trends , 2020, Neurocomputing.

[729]  Renato Umeton,et al.  Automated machine learning: Review of the state-of-the-art and opportunities for healthcare , 2020, Artif. Intell. Medicine.

[730]  Rudolf Allmann,et al.  The introduction of structure types into the Inorganic Crystal Structure Database ICSD , 2007, Acta crystallographica. Section A, Foundations of crystallography.

[731]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[732]  Carol Tenopir,et al.  New web services that help authors choose journals , 2017, Learn. Publ..

[733]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[734]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[735]  Samy Bengio,et al.  Tensor2Tensor for Neural Machine Translation , 2018, AMTA.

[736]  Alexander Wong,et al.  DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions , 2019, ArXiv.

[737]  David Lo,et al.  A Large Scale Study of Long-Time Contributor Prediction for GitHub Projects , 2021, IEEE Transactions on Software Engineering.

[738]  J. P. Vroom,et al.  Advances in Psychology. , 1998, Canadian Medical Association journal.

[739]  Nicolas Le Roux,et al.  Understanding the impact of entropy on policy optimization , 2018, ICML.

[740]  M. De Graef,et al.  EMsoft: open source software for electron diffraction/image simulations , 2017, Microscopy and Microanalysis.

[741]  Naoya Shibata,et al.  Theoretical framework of statistical noise in scanning transmission electron microscopy. , 2018, Ultramicroscopy.

[742]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[743]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[744]  Vijayan K. Asari,et al.  The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches , 2018, ArXiv.

[745]  M. Imani,et al.  FELIX , 2018, Proceedings of the International Conference on Computer-Aided Design.

[746]  Rosemarie Velik,et al.  Discrete Fourier Transform Computation Using Neural Networks , 2008, 2008 International Conference on Computational Intelligence and Security.

[747]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[748]  C. W. Han,et al.  Towards the low-dose characterization of beam sensitive nanostructures via implementation of sparse image acquisition in scanning transmission electron microscopy , 2017 .

[749]  K. Ishizuka,et al.  A new theoretical and practical approach to the multislice method , 1977 .

[750]  Haibin Ling,et al.  Revisiting Video Saliency Prediction in the Deep Learning Era , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[751]  Jurica Seva,et al.  QURATOR: Innovative Technologies for Content and Data Curation , 2020, Qurator.

[752]  Zhiqing Yang,et al.  Transmission Electron Microscopy (TEM) , 2017 .

[753]  Surya Ganguli,et al.  Exponential expressivity in deep neural networks through transient chaos , 2016, NIPS.

[754]  M. Date,et al.  A statistical model of signal-noise in scanning electron microscopy. , 2012, Scanning.

[755]  D. Sculley,et al.  Hidden Technical Debt in Machine Learning Systems , 2015, NIPS.

[756]  Yvan Saeys,et al.  Cost-Efficient Segmentation of Electron Microscopy Images Using Active Learning , 2019, BNAIC/BENELEARN.

[757]  Urban,et al.  Reconstruction of the projected crystal potential in transmission electron microscopy by means of a maximum-likelihood refinement algorithm , 2000, Acta crystallographica. Section A, Foundations of crystallography.

[758]  Bo Liu,et al.  DNN-based aberration correction in a wavefront sensorless adaptive optics system. , 2019, Optics express.

[759]  M. Malac,et al.  Radiation damage in the TEM and SEM. , 2004, Micron.

[760]  King Hann Lim,et al.  Review of Adaptive Activation Function in Deep Neural Network , 2018, 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[761]  Ting-Chun Wang,et al.  Partial Convolution based Padding , 2018, ArXiv.

[762]  E. Bonanno,et al.  Energy Dispersive X-ray (EDX) microanalysis: A powerful tool in biomedical research and diagnosis , 2018, European journal of histochemistry : EJH.

[763]  Renée J. Miller Big Data Curation , 2014, COMAD.

[764]  Liang Gu,et al.  An empirically tuned 2D and 3D FFT library on CUDA GPU , 2010, ICS '10.

[765]  Kazuo Ishizuka,et al.  Contrast transfer of crystal images in TEM , 1980 .

[766]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[767]  S. Beatson,et al.  Use of whole genome sequencing to investigate an increase in Neisseria gonorrhoeae infection among women in urban areas of Australia , 2018, Scientific Reports.

[768]  O. Scherzer Handbook of mathematical methods in imaging , 2011 .

[769]  E. M. Dogo,et al.  A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks , 2018, 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS).

[770]  H. Robbins A Stochastic Approximation Method , 1951 .

[771]  Levent Sagun,et al.  A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks , 2019, ICML.

[772]  Eric A. Stach,et al.  Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation , 2019, arXiv.org.

[773]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[774]  Julian Salazar,et al.  Transformers without Tears: Improving the Normalization of Self-Attention , 2019, ArXiv.

[775]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[776]  Keiron O'Shea,et al.  An Introduction to Convolutional Neural Networks , 2015, ArXiv.

[777]  Jonathan P. Tennant,et al.  The state of the art in peer review , 2018, FEMS microbiology letters.

[778]  Kaiming He,et al.  Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.

[779]  Joachim Weickert,et al.  Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings. , 2020, 2002.00638.

[780]  Nikhil Ketkar Introduction to Theano , 2017 .

[781]  Marco Baiesi Power Gradient Descent , 2019, ArXiv.

[782]  Omar Hernández Rodríguez,et al.  A semiotic reflection on the didactics of the Chain rule , 2010, The Mathematics Enthusiast.

[783]  M. Kramer Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[784]  Catia Trubiani,et al.  Model-Driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[785]  Volkan Cevher,et al.  Compressible Distributions for High-Dimensional Statistics , 2011, IEEE Transactions on Information Theory.

[786]  Ambika Gupta,et al.  Deploying an Application using Google Cloud Platform , 2020, 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

[787]  Yibo Zhang,et al.  Deep learning enhanced mobile-phone microscopy , 2017, ACS Photonics.

[788]  D. Zagorac,et al.  Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features , 2019, Journal of applied crystallography.

[789]  Zhen Wang,et al.  On the Effectiveness of Least Squares Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[790]  Syota Fujinaka,et al.  Understanding of scanning-system distortions of atomic-scale scanning transmission electron microscopy images for accurate lattice parameter measurements , 2020, Journal of Materials Science.

[791]  Adhemar Bultheel,et al.  Empirical Bayes Approach to Improve Wavelet Thresholding for Image Noise Reduction , 2001 .

[792]  Mohsen Nowkarizi,et al.  Evaluating the effectiveness of Google, Parsijoo, Rismoon, and Yooz to retrieve Persian documents , 2020, Libr. Hi Tech.

[793]  Venu Govindaraju,et al.  Why Regularized Auto-Encoders learn Sparse Representation? , 2015, ICML.

[794]  J. Thong,et al.  Effect of shot noise and secondary emission noise in scanning electron microscope images , 2004 .

[795]  Aydogan Ozcan,et al.  Resolution Enhancement in Scanning Electron Microscopy using Deep Learning , 2020 .

[796]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[797]  Bin Dong,et al.  Nostalgic Adam: Weighing more of the past gradients when designing the adaptive learning rate , 2019, IJCAI.

[798]  S. Wacławek,et al.  Microscopic Techniques for the Analysis of Micro and Nanostructures of Biopolymers and Their Derivatives , 2020, Polymers.

[799]  Jonathan Le Roux,et al.  Autoclip: Adaptive Gradient Clipping for Source Separation Networks , 2020, 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP).

[800]  Chunyu Chen,et al.  Cryo-electron microscope image denoising based on the geodesic distance , 2018, BMC Structural Biology.

[801]  Zunlei Feng,et al.  Neural Style Transfer: A Review , 2017, IEEE Transactions on Visualization and Computer Graphics.

[802]  Kim-Han Thung,et al.  A brief review on multi-task learning , 2018, Multimedia Tools and Applications.

[803]  Scott C. Douglas,et al.  Why RELU Units Sometimes Die: Analysis of Single-Unit Error Backpropagation in Neural Networks , 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

[804]  Mariette Hellenbrandt,et al.  The Inorganic Crystal Structure Database (ICSD)—Present and Future , 2004 .

[805]  Peyman Milanfar,et al.  Clustering-Based Denoising With Locally Learned Dictionaries , 2009, IEEE Transactions on Image Processing.

[806]  Zhiwei Steven Wu,et al.  Understanding Gradient Clipping in Private SGD: A Geometric Perspective , 2020, NeurIPS.

[807]  Pierre-Marc Jodoin,et al.  Prostate Cancer Semantic Segmentation by Gleason Score Group in mp-MRI with Self Attention Model on the Peripheral Zone , 2020 .

[808]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[809]  Pradeep Dubey,et al.  K-TanH: Efficient TanH For Deep Learning , 2019 .

[810]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[811]  Gerlind Plonka-Hoch,et al.  The Curvelet Transform , 2010, IEEE Signal Processing Magazine.

[812]  Majnu John,et al.  Regularized deep learning with a non-convex penalty , 2019, ArXiv.

[813]  Santiago Grijalva,et al.  A Review of Reinforcement Learning for Autonomous Building Energy Management , 2019, Comput. Electr. Eng..

[814]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[815]  Maximilien Kintz,et al.  Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark , 2020, 2020 2nd International Conference on Artificial Intelligence, Robotics and Control.

[816]  Johannes E. Schindelin,et al.  The ImageJ ecosystem: An open platform for biomedical image analysis , 2015, Molecular reproduction and development.

[817]  Carl Doersch,et al.  Tutorial on Variational Autoencoders , 2016, ArXiv.

[818]  Richard A. Vaia,et al.  Framework for nanocomposites , 2004 .

[819]  David A. Patterson,et al.  Motivation for and Evaluation of the First Tensor Processing Unit , 2018, IEEE Micro.

[820]  Sham M. Kakade,et al.  Provably Efficient Maximum Entropy Exploration , 2018, ICML.

[821]  Ole Winther,et al.  A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images , 2018, Advanced Theory and Simulations.

[822]  Tao Zhang,et al.  Learning Spectral Mapping for Speech Dereverberation and Denoising , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[823]  Jonathan L. Shapiro,et al.  Genetic Algorithms in Machine Learning , 2001, Machine Learning and Its Applications.

[824]  E. S. Ameh A review of basic crystallography and x-ray diffraction applications , 2019, The International Journal of Advanced Manufacturing Technology.

[825]  Jevin D. West,et al.  Scientific journals still matter in the era of academic search engines and preprint archives , 2019, J. Assoc. Inf. Sci. Technol..

[826]  Hanan Samet,et al.  Training Quantized Nets: A Deeper Understanding , 2017, NIPS.

[827]  Gary G. Yen,et al.  Particle swarm optimization of deep neural networks architectures for image classification , 2019, Swarm Evol. Comput..

[828]  Zhao Chen,et al.  GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks , 2017, ICML.

[829]  Ally Salim,et al.  Synthetic Patient Generation: A Deep Learning Approach Using Variational Autoencoders , 2018, ArXiv.

[830]  Dimensions Resources A Guide to the Dimensions Data Approach , 2018 .

[831]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[832]  Long Ji Lin,et al.  Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.

[833]  Jaakko Astola,et al.  Optimal weighted median filtering under structural constraints , 1995, IEEE Trans. Signal Process..

[834]  Lu Yuan,et al.  Dynamic ReLU , 2020, ECCV.

[835]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[836]  Ricardo Henriques,et al.  ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy , 2020, bioRxiv.

[837]  H. Sebastian Seung,et al.  Superhuman Accuracy on the SNEMI3D Connectomics Challenge , 2017, ArXiv.

[838]  Walid A. Salameh,et al.  Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning , 2019, 2019 10th International Conference on Information and Communication Systems (ICICS).

[839]  M. McCartney,et al.  Absolute measurement of normalized thickness, t/λi, from off-axis electron holography , 1994 .

[840]  Wenlong Fu,et al.  A Survey of Recent Advances in Transfer Learning , 2019, 2019 IEEE 19th International Conference on Communication Technology (ICCT).

[841]  Raziel Haimi-Cohen,et al.  Image Compression Based on Compressive Sensing: End-to-End Comparison With JPEG , 2017, IEEE Transactions on Multimedia.

[842]  Wolfgang Nejdl,et al.  How to Search the Internet Archive Without Indexing It , 2016, TPDL.

[843]  D. Dyck Persistent misconceptions about incoherence in electron microscopy , 2011 .

[844]  Florian Jug,et al.  Noise2Void - Learning Denoising From Single Noisy Images , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[845]  G. Dent,et al.  Modern Raman Spectroscopy: A Practical Approach , 2005 .

[846]  Kunihiko Fukushima,et al.  Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .

[847]  Jong Chul Ye,et al.  Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems , 2017, SIAM J. Imaging Sci..

[848]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[849]  Norman Meuschke,et al.  Academic Plagiarism Detection , 2019, ACM Comput. Surv..

[850]  R. Vasudevan,et al.  USID and Pycroscopy – Open Source Frameworks for Storing and Analyzing Imaging and Spectroscopy Data , 2019, Microscopy and Microanalysis.

[851]  M. Schallmey,et al.  Database Mining for Novel Bacterial β-Etherases, Glutathione-Dependent Lignin-Degrading Enzymes , 2019, Applied and Environmental Microbiology.

[852]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[853]  A. P. Sarath Chandar,et al.  PatchUp: A Regularization Technique for Convolutional Neural Networks , 2020, ArXiv.

[854]  Tolga Tasdizen,et al.  Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning , 2019, Science Advances.

[855]  Thomas Boudier,et al.  EM-net: Deep learning for electron microscopy image segmentation , 2020 .

[856]  Boris Hanin,et al.  Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients? , 2018, NeurIPS.

[857]  Olga Kononova,et al.  Unsupervised word embeddings capture latent knowledge from materials science literature , 2019, Nature.

[858]  Siddharth Krishna Kumar,et al.  On weight initialization in deep neural networks , 2017, ArXiv.

[859]  E. Musk An Integrated Brain-Machine Interface Platform With Thousands of Channels , 2019, Journal of medical Internet research.

[860]  Eni Mustafaraj,et al.  The case for voter-centered audits of search engines during political elections , 2020, FAT*.

[861]  Bogdan Vasilescu,et al.  The Signals that Potential Contributors Look for When Choosing Open-source Projects , 2019, Proc. ACM Hum. Comput. Interact..

[862]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[863]  D Van Dyck,et al.  MULTEM: A new multislice program to perform accurate and fast electron diffraction and imaging simulations using Graphics Processing Units with CUDA. , 2015, Ultramicroscopy.

[864]  Saulius Gražulis,et al.  Specification of the Crystallographic Information File format, version 2.0 , 2016 .

[865]  Duncan N. Johnstone,et al.  Electron Microscopy (Big and Small) Data Analysis With the Open Source Software Package HyperSpy , 2017, Microscopy and Microanalysis.

[866]  Nathan Srebro,et al.  Exploring Generalization in Deep Learning , 2017, NIPS.

[867]  H. Sánchez,et al.  Energy dispersive inelastic X-ray scattering spectroscopy – A review , 2019, Spectrochimica Acta Part B: Atomic Spectroscopy.

[868]  Hitoshi Kiya,et al.  Fixed Smooth Convolutional Layer for Avoiding Checkerboard Artifacts in CNNS , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[869]  P. Urbach On the utility of repeating the ‘same’ experiment , 1981 .

[870]  Suvrit Sra,et al.  Random Shuffling Beats SGD after Finite Epochs , 2018, ICML.

[871]  Sergei V. Kalinin,et al.  Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study , 2018, Science Advances.

[872]  Max Tegmark,et al.  Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.

[873]  Sivakumar Rajagopal,et al.  A robust anisotropic diffusion filter with low arithmetic complexity for images , 2019, EURASIP J. Image Video Process..

[874]  Xiang Chen,et al.  Deep learning in medical image registration , 2020, Progress in Biomedical Engineering.

[875]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[876]  Jeffrey M. Ede,et al.  Exit Wavefunction Reconstruction from Single Transmission Electron Micrographs with Deep Learning [pre-print] , 2020 .

[877]  Mark Harman,et al.  Machine Learning Testing: Survey, Landscapes and Horizons , 2019, IEEE Transactions on Software Engineering.

[878]  John B. Moore,et al.  Singular Value Decomposition , 1994 .

[879]  Hyunjung Shim,et al.  MGGAN: Solving Mode Collapse Using Manifold-Guided Training , 2018, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[880]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[881]  Brian Hutchinson,et al.  Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels , 2019, Scientific Reports.

[882]  Qingquan Song,et al.  Auto-Keras: An Efficient Neural Architecture Search System , 2018, KDD.

[883]  Jürgen Schmidhuber,et al.  Training Very Deep Networks , 2015, NIPS.

[884]  Luis Mateus Rocha,et al.  Singular value decomposition and principal component analysis , 2003 .

[885]  Jia Xu,et al.  Learning to See in the Dark , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[886]  B. Taylor,et al.  CODATA Recommended Values of the Fundamental Physical Constants: 2010 | NIST , 2005, 1203.5425.

[887]  Laurence T. Yang,et al.  A survey on deep learning for big data , 2018, Inf. Fusion.

[888]  V. Radmilović,et al.  Annular dark field imaging in a TEM , 2004 .

[889]  Daniel M. Johnson Lectures, Textbooks, Academic Calendar, and Administration: An Agenda for Change , 2018, The Uncertain Future of American Public Higher Education.

[890]  Xing Xu,et al.  Prediction of academic performance associated with internet usage behaviors using machine learning algorithms , 2019, Comput. Hum. Behav..

[891]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[892]  Torsten Hoefler,et al.  Augment your batch: better training with larger batches , 2019, ArXiv.

[893]  J. Biskupek,et al.  Electron dose dependence of signal-to-noise ratio, atom contrast and resolution in transmission electron microscope images. , 2014, Ultramicroscopy.

[894]  Gaëtan Hadjeres,et al.  Deep Learning Techniques for Music Generation , 2019 .

[895]  N. V. Subba Reddy,et al.  Effects of padding on LSTMs and CNNs , 2019, ArXiv.

[896]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[897]  Andrew S. Glassner,et al.  Graphics Gems , 1990 .

[898]  Majdi Maabreh,et al.  Parameters optimization of deep learning models using Particle swarm optimization , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[899]  V. Mlynárik Introduction to nuclear magnetic resonance. , 2017, Analytical biochemistry.

[900]  Isabella Peters,et al.  The relationship between bioRxiv preprints, citations and altmetrics , 2020, Quantitative Science Studies.

[901]  Kyunghyun Cho,et al.  Boltzmann Machines and Denoising Autoencoders for Image Denoising , 2013, ICLR.

[902]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[903]  Sergei V. Kalinin,et al.  Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) With Nonrectangular Scans , 2018, Microscopy and Microanalysis.

[904]  Yang Feng,et al.  Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications , 2018, WWW.

[905]  D. Bavelier,et al.  Exercising your brain: a review of human brain plasticity and training-induced learning. , 2008, Psychology and aging.

[906]  Wan Ahmad Tajuddin Wan Abdullah,et al.  Logic Learning in Hopfield Networks , 2008, ArXiv.

[907]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[908]  Serge J. Belongie,et al.  Residual Networks Behave Like Ensembles of Relatively Shallow Networks , 2016, NIPS.

[909]  Petri Koistinen,et al.  Using additive noise in back-propagation training , 1992, IEEE Trans. Neural Networks.

[910]  Dong Liu,et al.  Deep Learning-Based Video Coding: A Review and A Case Study , 2019, ArXiv.

[911]  Kaiyong Zhao,et al.  AutoML: A Survey of the State-of-the-Art , 2019, Knowl. Based Syst..

[912]  Stefano V. Albrecht,et al.  Stabilizing Generative Adversarial Networks: A Survey , 2019 .

[913]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[914]  Jin Woo Kim,et al.  Molecular generative model based on conditional variational autoencoder for de novo molecular design , 2018, Journal of Cheminformatics.

[915]  Liwei Wang,et al.  The Expressive Power of Neural Networks: A View from the Width , 2017, NIPS.

[916]  Ardan Patwardhan,et al.  EMPIAR: a public archive for raw electron microscopy image data , 2016, Nature Methods.

[917]  Shi-Min Hu,et al.  Global Contrast Based Salient Region Detection , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[918]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[919]  Ilya Kostrikov,et al.  Automatic Data Augmentation for Generalization in Deep Reinforcement Learning , 2020, ArXiv.

[920]  Yabo Fu,et al.  Deep Learning in Medical Image Registration: A Review , 2020, Physics in medicine and biology.

[921]  A. Ng Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.

[922]  Terence Soule,et al.  Genetic Programming: Theory and Practice , 2003 .

[923]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[924]  Harish G. Ramaswamy,et al.  Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[925]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[926]  Raimondo Schettini,et al.  Semantic Food Segmentation for Automatic Dietary Monitoring , 2018, 2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin).

[927]  Gunho Choi,et al.  Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. , 2018, Optics express.

[928]  Jeon Gue Park,et al.  Deep neural network using trainable activation functions , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[929]  Osama Alaidi,et al.  Electron Tomography: A Three‐Dimensional Analytic Tool for Hard and Soft Materials Research , 2015, Advanced materials.

[930]  Houqiang Li,et al.  Quantization Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[931]  Shuiwang Ji,et al.  Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation , 2017, IEEE Transactions on Medical Imaging.

[932]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[933]  Aydogan Ozcan,et al.  Resolution enhancement in scanning electron microscopy using deep learning , 2019, Scientific Reports.

[934]  Ola Spjuth,et al.  Deep Learning in Image Cytometry: A Review , 2018, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[935]  Hanqiang Cao,et al.  Nearest Neighbor Value Interpolation , 2012, ArXiv.

[936]  Oleksandr Borysenko,et al.  CoolMomentum: a method for stochastic optimization by Langevin dynamics with simulated annealing , 2020, Scientific Reports.

[937]  Earl J. Kirkland,et al.  Image Simulation in Transmission Electron Microscopy , 2006 .

[938]  Lewis D. Griffin,et al.  A New Angle on L2 Regularization , 2018, ArXiv.

[939]  O Anatole von Lilienfeld,et al.  Introducing Machine Learning: Science and Technology , 2020, Mach. Learn. Sci. Technol..

[940]  Jeffrey M. Ede,et al.  Partial Scanning Transmission Electron Microscopy with Deep Learning , 2020, Scientific Reports.

[941]  N. Browning,et al.  Implementing an accurate and rapid sparse sampling approach for low-dose atomic resolution STEM imaging , 2016 .

[942]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[943]  Rich Caruana,et al.  Do Deep Nets Really Need to be Deep? , 2013, NIPS.

[944]  Geoffrey E. Hinton,et al.  A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.

[945]  Jun Li,et al.  Shakeout: A New Approach to Regularized Deep Neural Network Training , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[946]  Prateek Jain,et al.  SGD without Replacement: Sharper Rates for General Smooth Convex Functions , 2019, ICML.

[947]  Arnaud Doucet,et al.  On the Impact of the Activation Function on Deep Neural Networks Training , 2019, ICML.

[948]  P. Nellist,et al.  Managing dose-, damage- and data-rates in multi-frame spectrum-imaging. , 2018, Microscopy.

[949]  J. Zico Kolter,et al.  Generalization in Deep Networks: The Role of Distance from Initialization , 2019, ArXiv.

[950]  S. Natarajan,et al.  A New Backpropagation Algorithm without Gradient Descent , 2018, ArXiv.

[951]  Yang Wang,et al.  BigDL: A Distributed Deep Learning Framework for Big Data , 2018, SoCC.

[952]  Carlos Oscar S Sorzano,et al.  Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy , 2018, IUCrJ.

[953]  Laurence Aitchison A statistical theory of semi-supervised learning , 2020, ArXiv.

[954]  C. Ophus A fast image simulation algorithm for scanning transmission electron microscopy , 2017, Advanced Structural and Chemical Imaging.

[955]  Sungjae Lee,et al.  Multitask Learning with Single Gradient Step Update for Task Balancing , 2020, ArXiv.

[956]  Kilian Q. Weinberger,et al.  On Feature Normalization and Data Augmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[957]  Feng Xia,et al.  Academic social networks: Modeling, analysis, mining and applications , 2019, J. Netw. Comput. Appl..

[958]  Gu-Yeon Wei,et al.  Benchmarking TPU, GPU, and CPU Platforms for Deep Learning , 2019, ArXiv.

[959]  Jingge Zhu Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful , 2020, UAI.

[960]  Mojtaba Soltanalian,et al.  Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding , 2019, ArXiv.

[961]  Kunal N. Chaudhury,et al.  Image denoising using optimally weighted bilateral filters: A sure and fast approach , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[962]  Roger G. Melko,et al.  QuCumber: wavefunction reconstruction with neural networks , 2018, SciPost Physics.

[963]  Mark Sellke,et al.  Approximating Continuous Functions by ReLU Nets of Minimal Width , 2017, ArXiv.

[964]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[965]  Ryan-Rhys Griffiths,et al.  Constrained Bayesian optimization for automatic chemical design using variational autoencoders , 2019, Chemical science.

[966]  Kyoung Mu Lee,et al.  Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[967]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[968]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[969]  Mourad Ouzzani,et al.  Data Curation with Deep Learning [Vision] , 2018 .

[970]  Yu Zhang,et al.  A Survey on Multi-Task Learning , 2017, IEEE Transactions on Knowledge and Data Engineering.

[971]  Hui Xiong,et al.  A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.

[972]  C. Dienemann,et al.  Transcription initiation complex structures elucidate DNA opening , 2016, Nature.

[973]  David Silver,et al.  Memory-based control with recurrent neural networks , 2015, ArXiv.

[974]  Giacomo Boracchi,et al.  Augmented Grad-CAM: Heat-Maps Super Resolution Through Augmentation , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[975]  Terrence J. Sejnowski,et al.  The Deep Learning Revolution , 2018 .

[976]  Alejandro Duran,et al.  The Ongoing Evolution of OpenMP , 2018, Proceedings of the IEEE.

[977]  L. Kourkoutis,et al.  Atomic-Resolution Cryo-STEM Across Continuously Variable Temperatures , 2020, Microscopy and Microanalysis.

[978]  Adolph I. Cohen,et al.  Rods and Cones , 1972 .

[979]  Jiri Matas,et al.  All you need is a good init , 2015, ICLR.

[980]  Muyuan Chen,et al.  A complete data processing workflow for CryoET and subtomogram averaging , 2019, Nature Methods.

[981]  Abbie Grotke Web Archiving at the Library of Congress. , 2011 .

[982]  Lydia Gauerhof,et al.  Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[983]  Jürgen Schmidhuber,et al.  Highway Networks , 2015, ArXiv.

[984]  Dacheng Tao,et al.  Shakeout: A New Regularized Deep Neural Network Training Scheme , 2016, AAAI.

[985]  Lu Lu,et al.  Dying ReLU and Initialization: Theory and Numerical Examples , 2019, Communications in Computational Physics.

[986]  Roland Siegwart,et al.  Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[987]  Kyogu Lee,et al.  Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net , 2020, ArXiv.

[988]  Junsheng Zhang,et al.  Mining Scientific and Technical Literature , 2020 .

[989]  Lei Yang,et al.  Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[990]  Neel Sundaresan,et al.  IntelliCode compose: code generation using transformer , 2020, ESEC/SIGSOFT FSE.

[991]  Benjamin Recht,et al.  A Tour of Reinforcement Learning: The View from Continuous Control , 2018, Annu. Rev. Control. Robotics Auton. Syst..

[992]  Xiaofeng Zhang,et al.  CPU versus GPU: which can perform matrix computation faster—performance comparison for basic linear algebra subprograms , 2018, Neural Computing and Applications.

[993]  Saulius Gražulis,et al.  Crystallography and Databases , 2017, Data Sci. J..

[994]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[995]  Solon Barocas,et al.  Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices , 2019, SSRN Electronic Journal.

[996]  Noise2Atom: unsupervised denoising for scanning transmission electron microscopy images , 2020, Applied Microscopy.

[997]  Yehia Elkhatib,et al.  Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection , 2019, ACM Trans. Embed. Comput. Syst..

[998]  D Wolf,et al.  Weighted simultaneous iterative reconstruction technique for single-axis tomography. , 2014, Ultramicroscopy.

[999]  Koray Kavukcuoglu,et al.  Learning word embeddings efficiently with noise-contrastive estimation , 2013, NIPS.

[1000]  Yuan Cao,et al.  Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks , 2018, ArXiv.

[1001]  Alexandru Telea,et al.  An Image Inpainting Technique Based on the Fast Marching Method , 2004, J. Graphics, GPU, & Game Tools.

[1002]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[1003]  Jia Wang,et al.  A Zernike-moment-based non-local denoising filter for cryo-EM images , 2013, Science China Life Sciences.

[1004]  Leon Gunther The Physics of Music and Color , 2011 .

[1005]  Yingbo Zhou Learning Deep Autoencoders without Layer-wise Training , 2014, ArXiv.

[1006]  M. Lehmann,et al.  Tutorial on Off-Axis Electron Holography , 2002, Microscopy and Microanalysis.

[1007]  Guillermo Sapiro,et al.  Navier-stokes, fluid dynamics, and image and video inpainting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[1008]  Jordan M. Malof,et al.  Distributed solar photovoltaic array location and extent dataset for remote sensing object identification , 2016, Scientific Data.

[1009]  H. B. Curry The method of steepest descent for non-linear minimization problems , 1944 .

[1010]  Lucas Theis,et al.  Lossy Image Compression with Compressive Autoencoders , 2017, ICLR.

[1011]  Cosmin Bonchis,et al.  Custom Extended Sobel Filters , 2019, ArXiv.

[1012]  Quan Z. Sheng,et al.  Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey , 2019 .

[1013]  Jian Yang,et al.  MemNet: A Persistent Memory Network for Image Restoration , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[1014]  F. Giustino,et al.  Electron-polaron dichotomy of charge carriers in perovskite oxides , 2020, Communications Physics.

[1015]  Nilanjan Ray,et al.  Automated Left Atrial Segmentation from Magnetic Resonance Image Sequences Using Deep Convolutional Neural Network with Autoencoder , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[1016]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[1017]  Renato Renner,et al.  Discovering physical concepts with neural networks , 2018, Physical review letters.

[1018]  Antonio J. Peña,et al.  Performance Evaluation of cuDNN Convolution Algorithms on NVIDIA Volta GPUs , 2019, IEEE Access.

[1019]  Xianwen Yu,et al.  VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders , 2019, IJCAI.

[1020]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[1021]  Gabriel Kreiman,et al.  XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization , 2020, PLoS Comput. Biol..

[1022]  Oge Marques,et al.  On the use of variable stride in convolutional neural networks , 2020, Multimedia Tools and Applications.

[1023]  Salima Hassas,et al.  A survey on intrinsic motivation in reinforcement learning , 2019, ArXiv.

[1024]  Frank Hutter,et al.  Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..

[1025]  Lei Zhang,et al.  FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising , 2017, IEEE Transactions on Image Processing.

[1026]  Woohyung Lim,et al.  Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network , 2018, PloS one.

[1027]  Kurt Tutschku,et al.  Recurrent Multilayer Perceptrons for Identification and Control: The Road to Applications , 1995 .

[1028]  Sven Apel,et al.  On the relation between Github communication activity and merge conflicts , 2019, Empirical Software Engineering.

[1029]  Sumeer Gul,et al.  Retrieval performance of Google, Yahoo and Bing for navigational queries in the field of "life science and biomedicine" , 2020, Data Technol. Appl..

[1030]  Lakshminarayanan Subramanian,et al.  Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[1031]  Leong-Chuan Kwek,et al.  Machine Learning meets Quantum Foundations: A Brief Survey , 2020 .

[1032]  C. Kisielowski,et al.  On the Reciprocity of TEM and STEM , 2005, Microscopy and Microanalysis.

[1033]  Jan Kautz,et al.  Unsupervised Image-to-Image Translation Networks , 2017, NIPS.

[1034]  Minsuk Kahng,et al.  CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization , 2020, IEEE transactions on visualization and computer graphics.

[1035]  Jean-Jacques Greffet,et al.  Field theory for generalized bidirectional reflectivity: derivation of Helmholtz’s reciprocity principle and Kirchhoff’s law , 1998 .

[1036]  Rama Vasudevan,et al.  Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. , 2017, ACS nano.

[1037]  H. Landau Sampling, data transmission, and the Nyquist rate , 1967 .

[1038]  Florian Jug,et al.  Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[1039]  Margaret West,et al.  2016 Atomic Spectrometry Update – a review of advances in X-ray fluorescence spectrometry and its applications , 2015 .

[1040]  Stephen P. Boyd,et al.  A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights , 2014, J. Mach. Learn. Res..

[1041]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[1042]  Geoffrey E. Hinton,et al.  3D Object Recognition with Deep Belief Nets , 2009, NIPS.

[1043]  R. Henderson,et al.  Detective quantum efficiency of electron area detectors in electron microscopy , 2009, Ultramicroscopy.

[1044]  D R G Mitchell,et al.  Scripting-customized microscopy tools for Digital Micrograph. , 2005, Ultramicroscopy.

[1045]  P. Sutter Scanning Tunneling Microscopy in Surface Science , 2019, Springer Handbook of Microscopy.

[1046]  Martin Wattenberg,et al.  GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation , 2018, IEEE Transactions on Visualization and Computer Graphics.

[1047]  Maxim Ziatdinov,et al.  Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables. , 2021, ACS applied materials & interfaces.

[1048]  Mykhaylo Yatsymirskyy,et al.  The Fast Fourier Transform Partitioning Scheme for GPU’s Computation Effectiveness Improvement , 2017 .

[1049]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[1050]  Ming Wu,et al.  D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[1051]  Dong Hye Ye,et al.  Dynamic X-ray diffraction sampling for protein crystal positioning. , 2017, Journal of synchrotron radiation.

[1052]  Hongchu Du A nonlinear filtering algorithm for denoising HR(S)TEM micrographs. , 2015, Ultramicroscopy.

[1053]  Gitta Kutyniok,et al.  1 . 2 Sparsity : A Reasonable Assumption ? , 2012 .

[1054]  S. Shi,et al.  Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models , 2019, ArXiv.

[1055]  Kenichi Yoshida,et al.  How GitHub Contributing.md Contributes to Contributors , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).

[1056]  Tomas E. Ward,et al.  Generative Adversarial Networks in Computer Vision , 2019, ACM Comput. Surv..

[1057]  Stamatios Lefkimmiatis,et al.  Universal Denoising Networks : A Novel CNN Architecture for Image Denoising , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[1058]  Thed N. van Leeuwen,et al.  State of Open Access penetration in universities worldwide , 2020, ArXiv.

[1059]  Donald R. Baer,et al.  Practical Guides for X-Ray Photoelectron Spectroscopy (XPS): First Steps in planning, conducting and reporting XPS measurements. , 2019, Journal of vacuum science & technology. A, Vacuum, surfaces, and films : an official journal of the American Vacuum Society.

[1060]  Richard Kijowski,et al.  Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging , 2018, Magnetic resonance in medicine.

[1061]  Cryo-analytical STEM of frozen, aqueous dispersions of nanoparticles. , 2019, Micron.

[1062]  Stefan Geiß,et al.  Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries , 2020, Information, Communication & Society.

[1063]  Hao Yu,et al.  Neural Network Learning Without Backpropagation , 2010, IEEE Transactions on Neural Networks.

[1064]  Xiaoou Tang,et al.  Surpassing Human-Level Face Verification Performance on LFW with GaussianFace , 2014, AAAI.

[1065]  Xiaojun Li,et al.  Correction of refractive index mismatch-induced aberrations under radially polarized illumination by deep learning. , 2020, Optics express.

[1066]  J. Zuo,et al.  On the beam selection and convergence in the Bloch wave method , 1995 .

[1067]  Davide Castelvecchi,et al.  Google unveils search engine for open data , 2018, Nature.

[1068]  Qingming Huang,et al.  Image Saliency Detection Video Saliency Detection Co-saliency Detection Temporal RGBD Saliency Detection Motion , 2018 .

[1069]  Alessandro Acquisti,et al.  An Experiment in Hiring Discrimination via Online Social Networks , 2020, Manag. Sci..

[1070]  Bo Zhao,et al.  Deep learning in clinical natural language processing: a methodical review , 2019, J. Am. Medical Informatics Assoc..

[1071]  Gitta Kutyniok,et al.  Expressivity of Deep Neural Networks , 2020, ArXiv.

[1072]  Taimoor Akhtar,et al.  Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates , 2016, AAAI.

[1073]  Zhiyong Lu,et al.  Scaling up data curation using deep learning: An application to literature triage in genomic variation resources , 2018, PLoS Comput. Biol..

[1074]  Fábio M. Bayer,et al.  An iterative wavelet threshold for signal denoising , 2019, Signal Process..

[1075]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[1076]  Pierre Baldi,et al.  Learning Activation Functions to Improve Deep Neural Networks , 2014, ICLR.

[1077]  Alex M. Thomson,et al.  Neocortical Layer 6, A Review , 2010, Front. Neuroanat..

[1078]  Yi-Hsuan Yang,et al.  Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting. , 2019 .

[1079]  Kandarpa Kumar Sarma,et al.  Deep Learning Based Semantic Segmentation Applied to Satellite Image , 2020 .

[1080]  Devansh Arpit,et al.  Is Joint Training Better for Deep Auto-Encoders? , 2014 .

[1081]  Benedikt Wirth,et al.  Joint Denoising and Distortion Correction for Atomic Column Detection in Scanning Transmission Electron Microscopy Images , 2017, Microscopy and Microanalysis.

[1082]  Stewart Worrall,et al.  Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving , 2018, IEEE Transactions on Intelligent Transportation Systems.

[1083]  Zijian Zhang,et al.  Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[1084]  Christoph Koch,et al.  Off-axis and inline electron holography: A quantitative comparison , 2010 .

[1085]  Tung Hsu Technique of reflection electron microscopy , 1992, Microscopy research and technique.

[1086]  J. Carazo,et al.  Denoising of high-resolution single-particle electron-microscopy density maps by their approximation using three-dimensional Gaussian functions. , 2016, Journal of structural biology.

[1087]  Yuhui Zheng,et al.  Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..

[1088]  D. Sculley,et al.  The ML test score: A rubric for ML production readiness and technical debt reduction , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[1089]  E. A. Wachter,et al.  Phase sensitive demodulation in multiphoton microscopy. , 2002, Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada.

[1090]  Evangelos Gogolides,et al.  Deep learning denoising of SEM images towards noise-reduced LER measurements , 2019, Microelectronic Engineering.

[1091]  Jiaying Liu,et al.  Demystifying Neural Style Transfer , 2017, IJCAI.

[1092]  Xiao Xiang Zhu,et al.  Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[1093]  Tyler Highlander,et al.  Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add , 2016, BMVC.

[1094]  Karol Antczak,et al.  On Regularization Properties of Artificial Datasets for Deep Learning , 2019, Computer Science and Mathematical Modelling.

[1095]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[1096]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[1097]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[1098]  Hesham Mostafa,et al.  Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks , 2019, IEEE Signal Processing Magazine.

[1099]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[1100]  Paul K. Mandal,et al.  Deep CNN-LSTM with Word Embeddings for News Headline Sarcasm Detection , 2019 .

[1101]  Christian Ledig,et al.  Is the deconvolution layer the same as a convolutional layer? , 2016, ArXiv.

[1102]  Patrick Judd,et al.  Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation , 2020, ArXiv.

[1103]  Jascha Sohl-Dickstein,et al.  A Mean Field Theory of Batch Normalization , 2019, ICLR.

[1104]  Ali Borji,et al.  Salient object detection: A survey , 2014, Computational Visual Media.

[1105]  J. Épp X-ray diffraction (XRD) techniques for materials characterization , 2016 .

[1106]  Jürgen Schmidhuber,et al.  Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..

[1107]  Mike Innes,et al.  Flux: Elegant machine learning with Julia , 2018, J. Open Source Softw..

[1108]  H. Greenspan,et al.  Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. , 2019, Radiology.

[1109]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[1110]  M. Mendenhall,et al.  High-precision measurement of the x-ray Cu Kα spectrum , 2016, Journal of physics. B, Atomic, molecular, and optical physics : an Institute of Physics journal.

[1111]  Hanno Scharr,et al.  Optimal operators in digital image processing , 2000 .

[1112]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[1113]  Nibaran Das,et al.  A Survey on Extreme Learning Machine and Evolution of Its Variants , 2018, RTIP2R.

[1114]  Chi Chen,et al.  Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom , 2020, 2002.10632.

[1115]  Guglielmo Mazzola,et al.  NetKet: A machine learning toolkit for many-body quantum systems , 2019, SoftwareX.

[1116]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[1117]  Stephen Marshall,et al.  Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.

[1118]  S. Amari,et al.  Competition and Cooperation in Neural Nets , 1982 .

[1119]  Christoph T Koch,et al.  Towards full-resolution inline electron holography. , 2014, Micron.

[1120]  Samet Oymak,et al.  Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks , 2019, AISTATS.

[1121]  David A. Strubbe,et al.  Deep learning and density-functional theory , 2018, Physical Review A.

[1122]  David Matthews Craft beautiful equations in Word with LaTeX , 2019, Nature.

[1123]  Stacy Konkiel,et al.  Dimensions: Bringing down barriers between scientometricians and data , 2020, Quantitative Science Studies.

[1124]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[1125]  David Chiang,et al.  Improving Lexical Choice in Neural Machine Translation , 2017, NAACL.

[1126]  Yifeng Chen,et al.  Large-scale FFT on GPU clusters , 2010, ICS '10.

[1127]  Pascal Getreuer,et al.  Linear Methods for Image Interpolation , 2011, Image Process. Line.

[1128]  Wolfgang Glänzel,et al.  The impact of preprints in Library and Information Science: an analysis of citations, usage and social attention indicators , 2020, Scientometrics.

[1129]  A. Ozcan,et al.  On the use of deep learning for computational imaging , 2019, NanoScience + Engineering.

[1130]  Kunihiko Fukushima,et al.  Neocognitron for handwritten digit recognition , 2003, Neurocomputing.

[1131]  Yanjun Ma,et al.  PaddlePaddle: An Open-Source Deep Learning Platform from Industrial Practice , 2019 .

[1132]  Grgoire Montavon,et al.  Neural Networks: Tricks of the Trade , 2012, Lecture Notes in Computer Science.

[1133]  Jianwei Miao,et al.  A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy , 2017, Advanced Structural and Chemical Imaging.

[1134]  Wolfgang Wahlster,et al.  New Horizons for a Data-Driven Economy , 2016, Springer International Publishing.

[1135]  Tao Mei,et al.  Memory Matching Networks for One-Shot Image Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[1136]  Premkumar T. Devanbu,et al.  A Survey of Machine Learning for Big Code and Naturalness , 2017, ACM Comput. Surv..

[1137]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[1138]  Soumyajit Poddar,et al.  FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA , 2020, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC).

[1139]  Farhan Ullah,et al.  Software plagiarism detection in multiprogramming languages using machine learning approach , 2018, Concurr. Comput. Pract. Exp..

[1140]  Chen Kong,et al.  Take it in your stride: Do we need striding in CNNs? , 2017, ArXiv.

[1141]  Cho-Jui Hsieh,et al.  Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding , 2018, ArXiv.

[1142]  T. Lu,et al.  Reflection high-energy electron diffraction measurements of reciprocal space structure of 2D materials , 2016, Nanotechnology.

[1143]  Introduction to Fourier Transforms for TEM and STEM , 2017 .

[1144]  Isabella Haberbosch,et al.  Software tools for automated transmission electron microscopy , 2018, Nature Methods.

[1145]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[1146]  K. Henrick,et al.  New electron microscopy database and deposition system. , 2002, Trends in biochemical sciences.

[1147]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[1148]  Quan Zhang,et al.  Image Denoising via Sparse Representation Over Grouped Dictionaries With Adaptive Atom Size , 2017, IEEE Access.

[1149]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[1150]  Runbin Shi,et al.  FTDL: An FPGA-tailored Architecture for Deep Learning Systems , 2020, FPGA.

[1151]  J. Mark Introduction to radial basis function networks , 1996 .

[1152]  D. Molodov,et al.  Determination of grain boundary mobility during recrystallization by statistical evaluation of electron backscatter diffraction measurements , 2016 .

[1153]  Brian S. Haney,et al.  Deep Reinforcement Learning Patents: An Empirical Survey , 2020 .

[1154]  Giuseppe Bianco,et al.  Toxic Code Snippets on Stack Overflow , 2018, IEEE Transactions on Software Engineering.

[1155]  A. Kirkland,et al.  Characterisation of the signal and noise transfer of CCD cameras for electron detection , 2000, Microscopy research and technique.

[1156]  James J. Little,et al.  A Simple Yet Effective Baseline for 3d Human Pose Estimation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[1157]  Alex Graves,et al.  Decoupled Neural Interfaces using Synthetic Gradients , 2016, ICML.

[1158]  Li Fei-Fei,et al.  Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[1159]  Joshua B. Tenenbaum,et al.  Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning , 2017, ArXiv.

[1160]  Michael Gusenbauer,et al.  Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases , 2018, Scientometrics.

[1161]  Alexei A. Efros,et al.  Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[1162]  G. Uhlenbeck,et al.  On the Theory of the Brownian Motion , 1930 .

[1163]  P. Cohen,et al.  Reflection high-energy electron diffraction , 2004 .

[1164]  Rayid Ghani,et al.  A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes , 2015, KDD.

[1165]  Jorge Nocedal,et al.  Optimization Methods for Large-Scale Machine Learning , 2016, SIAM Rev..

[1166]  H. K. Hartline,et al.  Physiology of Photoreceptor Organs , 1972, Handbook of Sensory Physiology.

[1167]  Allison Doerr,et al.  Cryo-electron tomography , 2016, Nature Methods.

[1168]  Jose Javier Gonzalez Ortiz,et al.  What is the State of Neural Network Pruning? , 2020, MLSys.

[1169]  T. Alford,et al.  Experimental methods in chemical engineering: X‐ray diffraction spectroscopy— XRD , 2020, The Canadian Journal of Chemical Engineering.

[1170]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[1171]  Zhou Fang,et al.  Serving deep neural networks at the cloud edge for vision applications on mobile platforms , 2019, MMSys.

[1172]  Markus Knauff,et al.  An Efficiency Comparison of Document Preparation Systems Used in Academic Research and Development , 2014, PloS one.

[1173]  Ida-Maria Sintorn,et al.  Super-Resolution Reconstruction of Transmission Electron Microscopy Images Using Deep Learning , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[1174]  Joakim Lindblad,et al.  Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[1175]  Sydney Hall,et al.  The Implementation and Evolution of STAR/CIF Ontologies: Interoperability and Preservation of Structured Data , 2016, Data Sci. J..

[1176]  Nitish D. Patel,et al.  Computationally Efficient Radial Basis Function , 2018, ICONIP.

[1177]  Guoliang Kang,et al.  Regularization in deep neural networks , 2019 .

[1178]  Xiaobin Zhang,et al.  A Combination of RNN and CNN for Attention-based Relation Classification , 2018 .

[1179]  B. L. Mehdi,et al.  A sub-sampled approach to extremely low-dose STEM , 2018 .

[1180]  Hong Lin Teaching and Learning Without a Textbook , 2019, The International Review of Research in Open and Distributed Learning.

[1181]  Jason Yosinski,et al.  Understanding Neural Networks via Feature Visualization: A survey , 2019, Explainable AI.

[1182]  Raman Arora,et al.  On the Implicit Bias of Dropout , 2018, ICML.

[1183]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[1184]  Shuai Li,et al.  Towards Understanding the Regularization of Adversarial Robustness on Neural Networks , 2020, ICML.

[1185]  Xiwei Liu,et al.  A Survey of Generative Adversarial Networks , 2018, 2018 Chinese Automation Congress (CAC).

[1186]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[1187]  Ken Turkowski,et al.  Filters for common resampling tasks , 1990 .

[1188]  A. Wilkinson,et al.  Mapping strains at the nanoscale using electron back scatter diffraction , 2009 .

[1189]  Karol Antczak,et al.  Deep Recurrent Neural Networks for ECG Signal Denoising , 2018, ArXiv.

[1190]  A. Sufian,et al.  Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey , 2020, Knowl. Based Syst..

[1191]  L. Longo,et al.  Explainable Artificial Intelligence: a Systematic Review , 2020, ArXiv.

[1192]  Katsuro Inoue,et al.  How do developers utilize source code from stack overflow? , 2018, Empirical Software Engineering.

[1193]  Mengjie Zhang,et al.  Evolving Deep Convolutional Neural Networks for Image Classification , 2017, IEEE Transactions on Evolutionary Computation.

[1194]  Harald C. Gall,et al.  When Code Completion Fails: A Case Study on Real-World Completions , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).

[1195]  Ohad Shamir,et al.  Without-Replacement Sampling for Stochastic Gradient Methods , 2016, NIPS.

[1196]  A. Herzing,et al.  Dose-rate-dependent damage of cerium dioxide in the scanning transmission electron microscope. , 2016, Ultramicroscopy.

[1197]  Matus Telgarsky,et al.  Benefits of Depth in Neural Networks , 2016, COLT.

[1198]  E. Iso,et al.  Measurement Uncertainty and Probability: Guide to the Expression of Uncertainty in Measurement , 1995 .

[1199]  Colin Ophus,et al.  Correcting nonlinear drift distortion of scanning probe and scanning transmission electron microscopies from image pairs with orthogonal scan directions. , 2016, Ultramicroscopy.

[1200]  D. J. Tuptewar,et al.  Robust exemplar based image and video inpainting for object removal and region filling , 2017, 2017 International Conference on Intelligent Computing and Control (I2C2).

[1201]  P. Hunter The reproducibility “crisis” , 2017, EMBO reports.

[1202]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[1203]  Robert J. Marks,et al.  An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification , 1987, NIPS.

[1204]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[1205]  Greg Mori,et al.  Adapting Grad-CAM for Embedding Networks , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[1206]  Jaehoon Lee,et al.  On Empirical Comparisons of Optimizers for Deep Learning , 2019, ArXiv.

[1207]  Zhenan Sun,et al.  A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications , 2020, IEEE Transactions on Knowledge and Data Engineering.

[1208]  Ivan Lazić,et al.  Phase contrast STEM for thin samples: Integrated differential phase contrast. , 2016, Ultramicroscopy.

[1209]  Omkar H. Damodare,et al.  Hybrid method for medical image denoising using Shearlet transform and bilateral filter , 2015, 2015 International Conference on Information Processing (ICIP).

[1210]  Pascal Vincent,et al.  Higher Order Contractive Auto-Encoder , 2011, ECML/PKDD.

[1211]  Mohit Bansal,et al.  Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? , 2020, ACL.

[1212]  Ozan Öktem,et al.  Mathematics of Electron Tomography , 2015, Handbook of Mathematical Methods in Imaging.

[1213]  Xiaohua Zhai,et al.  A Large-Scale Study on Regularization and Normalization in GANs , 2018, ICML.

[1214]  Danielle Azar,et al.  Searching for activation functions using a self-adaptive evolutionary algorithm , 2020, GECCO Companion.

[1215]  Jeffrey M. Ede,et al.  Exit Wavefunction Reconstruction from Single Transmisson Electron Micrographs with Deep Learning , 2020, ArXiv.

[1216]  S. Ram,et al.  Beyond Rayleigh's criterion: a resolution measure with application to single-molecule microscopy. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[1217]  Raquel Urtasun,et al.  Graph HyperNetworks for Neural Architecture Search , 2018, ICLR.

[1218]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[1219]  Wookey Lee,et al.  Patent prior art search using deep learning language model , 2020, IDEAS.

[1220]  E. Gibney Open journals that piggyback on arXiv gather momentum , 2016, Nature.

[1221]  Eugene W. Myers,et al.  Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks , 2017, MICCAI.

[1222]  Alexey Potapov,et al.  Genetic Algorithms with DNN-Based Trainable Crossover as an Example of Partial Specialization of General Search , 2017, AGI.

[1223]  Saulius Gražulis,et al.  Crystallography Open Database – an open-access collection of crystal structures , 2009, Journal of applied crystallography.

[1224]  Wei Wang,et al.  Deep Learning for Single Image Super-Resolution: A Brief Review , 2018, IEEE Transactions on Multimedia.

[1225]  Ruimao Zhang,et al.  Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct? , 2018, ArXiv.

[1226]  Kurt Keutzer,et al.  Spatially Parallel Convolutions , 2018, ICLR.

[1227]  Tian Xia,et al.  DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM , 2016, Journal of structural biology.

[1228]  Yuxi Li,et al.  Deep Reinforcement Learning: An Overview , 2017, ArXiv.

[1229]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[1230]  Jialin Peng,et al.  Adversarial-Prediction Guided Multi-Task Adaptation for Semantic Segmentation of Electron Microscopy Images , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[1231]  H. Friedrich,et al.  Quantification and optimization of ADF-STEM image contrast for beam-sensitive materials , 2018, Royal Society Open Science.

[1232]  Nisha Ahlawat,et al.  RAMAN SPECTROSCOPY: A REVIEW , 2014 .

[1233]  Sergei V. Kalinin,et al.  Precision controlled atomic resolution scanning transmission electron microscopy using spiral scan pathways , 2017, Scientific Reports.

[1234]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[1235]  Fan Zhang,et al.  Brief review of image denoising techniques , 2019, Visual Computing for Industry, Biomedicine, and Art.

[1236]  Min Gu,et al.  Direct determination of aberration functions in microscopy by an artificial neural network. , 2020, Optics express.

[1237]  Aaron C. Courville,et al.  Recurrent Batch Normalization , 2016, ICLR.

[1238]  Baozhong Liu,et al.  Overview of Image Denoising Based on Deep Learning , 2019, Journal of Physics: Conference Series.

[1239]  Manoranjan Paul,et al.  Enhanced Transfer Learning with ImageNet Trained Classification Layer , 2019, PSIVT.

[1240]  Asuman E. Ozdaglar,et al.  Why random reshuffling beats stochastic gradient descent , 2015, Mathematical Programming.

[1241]  Qiang Yang,et al.  An Overview of Multi-task Learning , 2018 .

[1242]  Mukul Kumar,et al.  Electron Backscatter Diffraction in Materials Science , 2000 .

[1243]  Nicole Gruber,et al.  Are GRU Cells More Specific and LSTM Cells More Sensitive in Motive Classification of Text? , 2020, Frontiers in Artificial Intelligence.

[1244]  Marc G. Bellemare,et al.  Dopamine: A Research Framework for Deep Reinforcement Learning , 2018, ArXiv.

[1245]  Yvan Saeys,et al.  An interactive ImageJ plugin for semi-automated image denoising in electron microscopy , 2020, Nature Communications.

[1246]  Tianbao Yang,et al.  Improved Dropout for Shallow and Deep Learning , 2016, NIPS.

[1247]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[1248]  Swarat Chaudhuri,et al.  Neural Sketch Learning for Conditional Program Generation , 2017, ICLR.

[1249]  Andrew McCallum,et al.  Energy and Policy Considerations for Deep Learning in NLP , 2019, ACL.

[1250]  S P Rallison,et al.  What are Journals for? , 2015, Annals of the Royal College of Surgeons of England.

[1251]  Kenneth Moreland,et al.  The FFT on a GPU , 2003, HWWS '03.

[1252]  R. Tromp,et al.  Aberration corrected spin polarized low energy electron microscope. , 2020, Ultramicroscopy.

[1253]  Anastasios Kyrillidis,et al.  Demon: Momentum Decay for Improved Neural Network Training , 2021 .

[1254]  G. Deng,et al.  An adaptive Gaussian filter for noise reduction and edge detection , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[1255]  Hang Su,et al.  Learning Reliable Visual Saliency For Model Explanations , 2020, IEEE Transactions on Multimedia.

[1256]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[1257]  Tristan Bepler,et al.  Topaz-Denoise: general deep denoising models for cryoEM and cryoET , 2019, Nature Communications.

[1258]  L. Hultman,et al.  X-ray photoelectron spectroscopy: Towards reliable binding energy referencing , 2020, Progress in Materials Science.

[1259]  Frank Seide Keynote: The computer science behind the Microsoft Cognitive Toolkit: An open source large-scale deep learning toolkit for Windows and Linux , 2017, CGO 2017.

[1260]  Hong Zhang,et al.  Facial expression recognition via learning deep sparse autoencoders , 2018, Neurocomputing.

[1261]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[1262]  Takuya Akiba,et al.  Optuna: A Next-generation Hyperparameter Optimization Framework , 2019, KDD.

[1263]  Sek Chai,et al.  Bit Efficient Quantization for Deep Neural Networks , 2019, ArXiv.

[1264]  S. Levine,et al.  Accelerating Online Reinforcement Learning with Offline Datasets , 2020, ArXiv.

[1265]  Wlodek Zadrozny,et al.  Patent retrieval: a literature review , 2017, Knowledge and Information Systems.

[1266]  Jürgen Schmidhuber,et al.  Highway and Residual Networks learn Unrolled Iterative Estimation , 2016, ICLR.

[1267]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[1268]  J Barthel,et al.  Dr. Probe: A software for high-resolution STEM image simulation. , 2018, Ultramicroscopy.

[1269]  Hassan Khotanlou,et al.  An Empirical Study on Position of the Batch Normalization Layer in Convolutional Neural Networks , 2019, 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS).

[1270]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[1271]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[1272]  Yanrong Guo,et al.  A Brief Survey on Semantic Segmentation with Deep Learning , 2020, Neurocomputing.

[1273]  Su-Yun Huang,et al.  Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy , 2019 .

[1274]  Amy K. Hoover,et al.  Notes on Using Google Colaboratory in AI Education , 2020, ITiCSE.

[1275]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[1276]  Sebastian Caldas,et al.  LEAF: A Benchmark for Federated Settings , 2018, ArXiv.

[1277]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[1278]  S. Yao,et al.  Scanning distortion correction in STEM images. , 2018, Ultramicroscopy.

[1279]  Syed Muhammad Anwar,et al.  Medical Image Analysis using Convolutional Neural Networks: A Review , 2017, Journal of Medical Systems.

[1280]  Shiliang Sun,et al.  Multiview Transfer Learning and Multitask Learning , 2019, Multiview Machine Learning.

[1281]  Dimensons Team,et al.  A Guide to the Dimensions Data Approach , 2018 .

[1282]  Michael I. Jordan,et al.  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems , 1994, NIPS.

[1283]  Haibo He,et al.  Variational autoencoder based synthetic data generation for imbalanced learning , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[1284]  Kate Saenko,et al.  Unsupervised Video-to-Video Translation , 2018, ArXiv.

[1285]  P. Nellist,et al.  Unscrambling Mixed Elements using High Angle Annular Dark Field Scanning Transmission Electron Microscopy. , 2016, Physical review letters.

[1286]  Sheng Bi,et al.  Lightningnet: Fast and Accurate Semantic Segmentation for Autonomous Driving Based on 3D LIDAR Point Cloud , 2020, 2020 IEEE International Conference on Multimedia and Expo (ICME).

[1287]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[1288]  José Ranilla,et al.  Particle swarm optimization for hyper-parameter selection in deep neural networks , 2017, GECCO.

[1289]  Maja Pantic,et al.  Efficient N-Dimensional Convolutions via Higher-Order Factorization , 2019, ArXiv.

[1290]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[1291]  Narendra Chaudhary,et al.  Line roughness estimation and Poisson denoising in scanning electron microscope images using deep learning , 2019, Journal of Micro/Nanolithography, MEMS, and MOEMS.

[1292]  Peng Peng,et al.  Unsupervised Anomaly Detection Using Variational Auto-Encoder based Feature Extraction , 2019, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM).

[1293]  Yun Xu,et al.  On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning , 2018, Journal of Analysis and Testing.

[1294]  Joshua Romero,et al.  Exascale Deep Learning for Scientific Inverse Problems , 2019, ArXiv.

[1295]  L. Rayleigh Investigations in optics, with special reference to the spectroscope , 1880 .

[1296]  Joonho Lee,et al.  ProbAct: A Probabilistic Activation Function for Deep Neural Networks , 2019, ArXiv.

[1297]  Olivier Debeir,et al.  Robust Perceptual Night Vision in Thermal Colorization , 2020, VISIGRAPP.

[1298]  M. Hocevar,et al.  Shot-Noise-Limited Nanomechanical Detection and Radiation Pressure Backaction from an Electron Beam. , 2018, Physical review letters.

[1299]  M. Shackley X-Ray Fluorescence Spectrometry (XRF) , 2018, The Encyclopedia of Archaeological Sciences.

[1300]  Xin Yang,et al.  Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes , 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[1301]  Surya Ganguli,et al.  Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.

[1302]  Ömer Faruk Ertugrul,et al.  A novel type of activation function in artificial neural networks: Trained activation function , 2018, Neural Networks.

[1303]  Naftali Tishby,et al.  Machine learning and the physical sciences , 2019, Reviews of Modern Physics.

[1304]  Tapani Raiko,et al.  Semi-supervised Learning with Ladder Networks , 2015, NIPS.

[1305]  Hongming Shan,et al.  Deep-learning-based Breast CT for Radiation Dose Reduction , 2019, Developments in X-Ray Tomography XII.

[1306]  Martin Vetterli,et al.  Fast Fourier transforms: a tutorial review and a state of the art , 1990 .

[1307]  Edward Mackinnon De Broglie’s thesis: A critical retrospective , 1976 .

[1308]  Julian Togelius,et al.  Evolving Memory Cell Structures for Sequence Learning , 2009, ICANN.

[1309]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[1310]  Behnam Neyshabur,et al.  Towards Learning Convolutions from Scratch , 2020, NeurIPS.

[1311]  Spyros Kotoulas,et al.  Medical Text Classification using Convolutional Neural Networks , 2017, Studies in health technology and informatics.

[1312]  Chuang Gan,et al.  Once for All: Train One Network and Specialize it for Efficient Deployment , 2019, ICLR.

[1313]  Yu-Ju Lin,et al.  An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images , 2018, Journal of Digital Imaging.

[1314]  Timothy Lillicrap,et al.  Deep Compressed Sensing , 2019, ICML.

[1315]  Hua,et al.  Deep Learning for Super-Resolution in a Field Emission Scanning Electron Microscope , 2019, AI.

[1316]  A. Putnis,et al.  Off‐axis electron holography of magnetic nanowires and chains, rings, and planar arrays of magnetic nanoparticles , 2004, Microscopy research and technique.

[1317]  V. Matolín,et al.  Reflection high-energy electron loss spectroscopy (RHEELS): a new approach in the investigation of epitaxial thin film growth by reflection high-energy electron diffraction (RHEED) , 2003 .

[1318]  Romain Dolbeau,et al.  Theoretical peak FLOPS per instruction set: a tutorial , 2017, The Journal of Supercomputing.

[1319]  David A. Forsyth,et al.  Shape, Contour and Grouping in Computer Vision , 1999, Lecture Notes in Computer Science.

[1320]  Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics. , 2020, Physical review letters.

[1321]  P. Ganesh Kumar,et al.  Optimization of Metascheduler for Cloud Machine Learning Services , 2020, Wirel. Pers. Commun..

[1322]  C. Jacobsen,et al.  Relative merits and limiting factors for x-ray and electron microscopy of thick, hydrated organic materials. , 2020, Ultramicroscopy.

[1323]  Hai Nguyen,et al.  Review of Deep Reinforcement Learning for Robot Manipulation , 2019, 2019 Third IEEE International Conference on Robotic Computing (IRC).

[1324]  C. Adachi,et al.  CORRIGENDUM: Promising operational stability of high-efficiency organic light-emitting diodes based on thermally activated delayed fluorescence , 2014, Scientific Reports.

[1325]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[1326]  J. Harris Transmission electron microscopy in molecular structural biology: A historical survey. , 2015, Archives of biochemistry and biophysics.

[1327]  Sergey Levine,et al.  Reinforcement Learning with Deep Energy-Based Policies , 2017, ICML.

[1328]  David Patterson,et al.  MLPerf Training Benchmark , 2019, MLSys.

[1329]  Razvan Pascanu,et al.  A simple neural network module for relational reasoning , 2017, NIPS.

[1330]  Wolfgang Braun,et al.  Applied RHEED: Reflection High-Energy Electron Diffraction During Crystal Growth , 1999 .

[1331]  Development of a deep learning-based method to identify “good” regions of a cryo-electron microscopy grid , 2020, Biophysical Reviews.

[1332]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[1333]  D. Vaux,et al.  Replicates and repeats—what is the difference and is it significant? , 2012, EMBO reports.

[1334]  Kent J. Griffith,et al.  Recent Advances in Solid-State Nuclear Magnetic Resonance Techniques for Materials Research , 2020 .

[1335]  K. Furuya,et al.  X-ray analysis and mapping by wavelength dispersive X-ray spectroscopy in an electron microscope. , 2008, Ultramicroscopy.

[1336]  Yuan Cao,et al.  Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks , 2019, NeurIPS.

[1337]  S. Levine,et al.  Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems , 2020, ArXiv.

[1338]  G. R. Schleder,et al.  From DFT to machine learning: recent approaches to materials science–a review , 2019, Journal of Physics: Materials.

[1339]  James C. R. Whittington,et al.  Theories of Error Back-Propagation in the Brain , 2019, Trends in Cognitive Sciences.

[1340]  Yingli Tian,et al.  Coarse-to-Fine Semantic Segmentation From Image-Level Labels , 2018, IEEE Transactions on Image Processing.

[1341]  Ohad Shamir,et al.  The Power of Depth for Feedforward Neural Networks , 2015, COLT.

[1342]  Luís Torgo,et al.  OpenML: networked science in machine learning , 2014, SKDD.

[1343]  S. Valaee,et al.  Survey of Dropout Methods for Deep Neural Networks , 2019, ArXiv.

[1344]  B. Karthikeyan,et al.  Survey on FPGA Architecture and Recent Applications , 2019, 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN).

[1345]  Jing Liu,et al.  Effective Training of Convolutional Neural Networks With Low-Bitwidth Weights and Activations , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[1346]  Yibo Zhang,et al.  Phase recovery and holographic image reconstruction using deep learning in neural networks , 2017, Light: Science & Applications.

[1347]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[1348]  Xuelong Li,et al.  Deep neural networks with Elastic Rectified Linear Units for object recognition , 2018, Neurocomputing.

[1349]  D. Maneuski,et al.  Pixelated detectors and improved efficiency for magnetic imaging in STEM differential phase contrast. , 2016, Ultramicroscopy.

[1350]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[1351]  E. Pomarico,et al.  Ultrafast electron energy-loss spectroscopy in transmission electron microscopy , 2018, MRS Bulletin.

[1352]  Michael C. Mozer,et al.  Discrete Event, Continuous Time RNNs , 2017, ArXiv.

[1353]  Hyrum S. Anderson,et al.  Sparse imaging for fast electron microscopy , 2013, Electronic Imaging.

[1354]  Wei Shen,et al.  Weight Standardization , 2019, ArXiv.

[1355]  Misha Denil,et al.  Noisy Activation Functions , 2016, ICML.

[1356]  S. Gambhir,et al.  Noninvasive molecular imaging of small living subjects using Raman spectroscopy , 2008, Proceedings of the National Academy of Sciences.

[1357]  Yoshua Bengio,et al.  An empirical analysis of dropout in piecewise linear networks , 2013, ICLR.

[1358]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[1359]  K. Morrison Characterisation Methods in Solid State and Materials Science , 2019 .

[1360]  H. van der Sijs,et al.  The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: protocol for a cluster randomized stepped-wedge trial , 2019, BMC Medical Informatics and Decision Making.

[1361]  Michael R. Lyu,et al.  An Empirical Study of Common Challenges in Developing Deep Learning Applications , 2019, 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE).

[1362]  Sepp Hochreiter,et al.  Learning to Learn Using Gradient Descent , 2001, ICANN.

[1363]  Koray Kavukcuoglu,et al.  Multiple Object Recognition with Visual Attention , 2014, ICLR.

[1364]  Butler W. Lampson,et al.  There’s plenty of room at the Top: What will drive computer performance after Moore’s law? , 2020, Science.

[1365]  Eirini Kalliamvakou,et al.  Understanding "watchers" on GitHub , 2014, MSR 2014.

[1366]  A. Wilkinson,et al.  High-resolution elastic strain measurement from electron backscatter diffraction patterns: new levels of sensitivity. , 2006, Ultramicroscopy.

[1367]  Daniel Sonntag,et al.  A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces , 2018, ArXiv.

[1368]  Anitha Priya Krishnan,et al.  Optical Aberration Correction via Phase Diversity and Deep Learning , 2020, bioRxiv.

[1369]  Madhura Purnaprajna,et al.  Recurrent Neural Networks: An Embedded Computing Perspective , 2019, IEEE Access.

[1370]  A. List Descending through a Crowded Valley , 2021 .

[1371]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[1372]  Rakhi Nangia,et al.  Resource Utilization Optimization with Design Alternatives in FPGA based Arithmetic Logic Unit Architectures , 2018 .

[1373]  Mark Adam Dyson Advances in computational methods for transmission electron microscopy simulation and image processing , 2014 .

[1374]  G. Pozzi,et al.  Young’s double-slit interference experiment with electrons , 2007 .

[1375]  Tom Schaul,et al.  Prioritized Experience Replay , 2015, ICLR.

[1376]  Xinfeng Zhang,et al.  Image and Video Compression With Neural Networks: A Review , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[1377]  Pradeep Dubey,et al.  Distributed Deep Learning Using Synchronous Stochastic Gradient Descent , 2016, ArXiv.

[1378]  J. Verbeeck,et al.  Comparison of first moment STEM with conventional differential phase contrast and the dependence on electron dose. , 2019, Ultramicroscopy.

[1379]  Jong Chul Ye,et al.  Compressed sensing MRI: a review from signal processing perspective , 2019, BMC biomedical engineering.

[1380]  Toby Green,et al.  Is open access affordable? Why current models do not work and why we need internet‐era transformation of scholarly communications , 2019, Learn. Publ..

[1381]  Alexander Gasnikov,et al.  Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping , 2020, NeurIPS.

[1382]  Dayong Wang,et al.  Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.

[1383]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[1384]  Richard Henderson,et al.  The energy dependence of contrast and damage in electron cryomicroscopy of biological molecules , 2019, Ultramicroscopy.

[1385]  D. Fitzpatrick The functional organization of local circuits in visual cortex: insights from the study of tree shrew striate cortex. , 1996, Cerebral cortex.

[1386]  Shuai Li,et al.  Lensless computational imaging through deep learning , 2017, ArXiv.

[1387]  Quoc V. Le,et al.  Neural Optimizer Search with Reinforcement Learning , 2017, ICML.

[1388]  Sunil Agrawal,et al.  An Efficient Image Denoising Scheme for Higher Noise Levels Using Spatial Domain Filters , 2018, Biomedical and Pharmacology Journal.

[1389]  J. Hughey,et al.  Releasing a preprint is associated with more attention and citations for the peer-reviewed article , 2019, eLife.

[1390]  Shiliang Sun,et al.  A Survey of Optimization Methods From a Machine Learning Perspective , 2019, IEEE Transactions on Cybernetics.

[1391]  Benjamin D. Wandelt,et al.  Wiener filter reloaded: fast signal reconstruction without preconditioning , 2017, 1702.08852.

[1392]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[1393]  Yi Wang,et al.  Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes , 2020, Comput. Methods Programs Biomed..

[1394]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[1395]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[1396]  Alan D. Mighell,et al.  NIST Crystallographic Databases for Research and Analysis , 1996, Journal of research of the National Institute of Standards and Technology.

[1397]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[1398]  Zachary Chase Lipton A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[1399]  Mohamad Ivan Fanany,et al.  Simulated Annealing Algorithm for Deep Learning , 2015 .

[1400]  Xiangnan Wang,et al.  Fast phase retrieval in off-axis digital holographic microscopy through deep learning. , 2018, Optics express.

[1401]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[1402]  Philipp Slusallek,et al.  How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality? , 2018, Ultramicroscopy.

[1403]  Honglak Lee,et al.  Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units , 2016, ICML.

[1404]  Yann LeCun,et al.  Recurrent Orthogonal Networks and Long-Memory Tasks , 2016, ICML.

[1405]  François Pachet,et al.  Deep learning for music generation: challenges and directions , 2018, Neural Comput. Appl..

[1406]  N. Tanaka Electron Nano-Imaging: Basics of Imaging and Diffraction for TEM and STEM Nobuo Tanaka , 2018 .

[1407]  Dongyu Liu,et al.  TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis , 2019, IEEE Transactions on Visualization and Computer Graphics.

[1408]  Benjamin Graham,et al.  Fractional Max-Pooling , 2014, ArXiv.

[1409]  Ausif Mahmood,et al.  Review of Deep Learning Algorithms and Architectures , 2019, IEEE Access.

[1410]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[1411]  I. Vesper Peer reviewers unmasked: largest global survey reveals trends , 2018, Nature.

[1412]  Kyle Siler,et al.  The pricing of open access journals: Diverse niches and sources of value in academic publishing , 2020, Quantitative Science Studies.

[1413]  Eero P. Simoncelli,et al.  Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy , 2020, IEEE Transactions on Computational Imaging.

[1414]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[1415]  G. Evans,et al.  Learning to Optimize , 2008 .

[1416]  Xu Sun,et al.  Adaptive Gradient Methods with Dynamic Bound of Learning Rate , 2019, ICLR.

[1417]  David M. Dutton,et al.  A review of machine learning , 1997, The Knowledge Engineering Review.

[1418]  Vinay Kumar,et al.  Linearized sigmoidal activation: A novel activation function with tractable non-linear characteristics to boost representation capability , 2019, Expert Syst. Appl..

[1419]  P. Jin,et al.  Correction of image drift and distortion in a scanning electron microscopy , 2015, Journal of microscopy.

[1420]  K. Nagao,et al.  Experimental Observation of Quasicrystal Growth. , 2015, Physical Review Letters.

[1421]  Dong Si,et al.  Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps , 2019, Molecules.

[1422]  David A. Patterson,et al.  In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[1423]  Kenichi Ohki,et al.  Natural images are reliably represented by sparse and variable populations of neurons in visual cortex , 2020, Nature Communications.

[1424]  T. Thireou,et al.  Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[1425]  Rui Henriques,et al.  Triclustering Algorithms for Three-Dimensional Data Analysis , 2018, ACM Comput. Surv..

[1426]  Suvrit Sra,et al.  Small nonlinearities in activation functions create bad local minima in neural networks , 2018, ICLR.

[1427]  Ti Bai,et al.  Probabilistic self-learning framework for Low-dose CT Denoising , 2020, ArXiv.

[1428]  Oren Barkan,et al.  Adaptive Compressed Tomography Sensing , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[1429]  Gintare Karolina Dziugaite,et al.  Revisiting generalization for deep learning: PAC-Bayes, flat minima, and generative models , 2019 .

[1430]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[1431]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[1432]  Paul A Midgley,et al.  Denoising time-resolved microscopy image sequences with singular value thresholding. , 2017, Ultramicroscopy.

[1433]  Yuan Tang,et al.  TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning , 2016, ArXiv.

[1434]  Liyuan Liu,et al.  On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.

[1435]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[1436]  Martin Wattenberg,et al.  How to Use t-SNE Effectively , 2016 .

[1437]  G. Debreczeni,et al.  Neural Network Exchange Format , 2019 .

[1438]  E. LeDell,et al.  H2O AutoML: Scalable Automatic Machine Learning , 2020 .

[1439]  Herke van Hoof,et al.  Addressing Function Approximation Error in Actor-Critic Methods , 2018, ICML.

[1440]  Maik Riechert,et al.  Fast and Memory-Efficient Neural Code Completion , 2020, 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR).

[1441]  Marco Aurélio Gerosa,et al.  More Common Than You Think: An In-depth Study of Casual Contributors , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).

[1442]  Vineeth N. Balasubramanian,et al.  Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[1443]  Alan Anwer Abdulla,et al.  Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism , 2020 .

[1444]  Isaac Amidror,et al.  Sub-Nyquist artefacts and sampling moiré effects , 2015, Royal Society Open Science.

[1445]  Kenneth O. Stanley,et al.  Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning , 2017, ArXiv.

[1446]  Razvan Pascanu,et al.  Advances in optimizing recurrent networks , 2012, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[1447]  Tomaso A. Poggio,et al.  Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning , 2016, ArXiv.

[1448]  N. Hondow,et al.  Low dose scanning transmission electron microscopy of organic crystals by scanning moiré fringes. , 2019, Micron.

[1449]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[1450]  B. Wandelt,et al.  Efficient Wiener filtering without preconditioning , 2012, 1210.4931.

[1451]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

[1452]  John D. Westbrook,et al.  EMDataBank.org: unified data resource for CryoEM , 2010, Nucleic Acids Res..

[1453]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[1454]  A. Musgrave Popper and ‘diminishing returns from repeated tests' , 1975 .

[1455]  Sayak Paul,et al.  A review of deep learning with special emphasis on architectures, applications and recent trends , 2020, Knowl. Based Syst..