A survey on deep learning and its applications
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[1] Ming Yang,et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Andrew Zisserman,et al. On-the-fly learning for visual search of large-scale image and video datasets , 2015, International Journal of Multimedia Information Retrieval.
[3] Y. J. Zhang,et al. A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..
[4] David G. Kleinbaum,et al. Analysis of Matched Data Using Logistic Regression , 2010 .
[5] Xiqun Chen,et al. Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach , 2017, ArXiv.
[6] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling , 2015, CVPR 2015.
[7] G.B. Coleman,et al. Image segmentation by clustering , 1979, Proceedings of the IEEE.
[8] Daniel M. Kane,et al. Robust Learning of Fixed-Structure Bayesian Networks , 2016, NeurIPS.
[9] C. Mood. Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It , 2010 .
[10] Jian Cheng,et al. Visualizing deep neural network by alternately image blurring and deblurring , 2018, Neural Networks.
[11] Samy Bengio,et al. Neural Combinatorial Optimization with Reinforcement Learning , 2016, ICLR.
[12] Atsushi Shimada,et al. Analytics of Deep Neural Network-Based Background Subtraction , 2018, J. Imaging.
[13] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .
[14] N. Rajpoot,et al. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images , 2016, IEEE Transactions on Medical Imaging.
[15] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[16] Ming Zhou,et al. Adaptive Multi-Compositionality for Recursive Neural Models with Applications to Sentiment Analysis , 2014, AAAI.
[17] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[18] Martin A. Fischler,et al. The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.
[19] Ben Glocker,et al. Spectral Graph Convolutions for Population-based Disease Prediction , 2017, MICCAI.
[20] Fangchun Yang,et al. A Traffic Prediction Enabled Double Rewarded Value Iteration Network for Route Planning , 2019, IEEE Transactions on Vehicular Technology.
[21] Pascal Poupart,et al. Unsupervised Video Object Segmentation for Deep Reinforcement Learning , 2018, NeurIPS.
[22] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[23] S. Roth,et al. Lightweight Probabilistic Deep Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Pascal Frossard,et al. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.
[25] Nicol N. Schraudolph,et al. Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent , 2002, Neural Computation.
[26] Jure Leskovec,et al. Modeling polypharmacy side effects with graph convolutional networks , 2018, bioRxiv.
[27] Hiroshi Sawada,et al. Predicting Destinations from Partial Trajectories Using Recurrent Neural Network , 2017, PAKDD.
[28] Thierry Bouwmans,et al. Background Subtraction in Real Applications: Challenges, Current Models and Future Directions , 2019, Comput. Sci. Rev..
[29] D. O. Hebb,et al. The organization of behavior , 1988 .
[30] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[31] P. Sivakumar,et al. A REVIEW ON IMAGE SEGMENTATION TECHNIQUES , 2016 .
[32] Mario Ignacio Chacon Murguia,et al. Self-organizing retinotopic maps applied to background modeling for dynamic object segmentation in video sequences , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[33] Karl J. Friston,et al. How Robust are Deep Neural Networks? , 2018, ArXiv.
[34] Yuan Fang,et al. Deep Video Foreground Target Extraction with Complex Scenes , 2018, 2018 International Conference on Sensor Networks and Signal Processing (SNSP).
[35] Ronan Collobert,et al. Recurrent Convolutional Neural Networks for Scene Labeling , 2014, ICML.
[36] Daniel Crevier,et al. Ai: The Tumultuous History Of The Search For Artificial Intelligence , 1993 .
[37] Wenwu Zhu,et al. Deep Learning on Graphs: A Survey , 2018, IEEE Transactions on Knowledge and Data Engineering.
[38] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[39] Roberto Cipolla,et al. Convolutional CRFs for Semantic Segmentation , 2018, BMVC.
[40] Meisam Razaviyayn,et al. Learning Deep Models: Critical Points and Local Openness , 2018, ICLR.
[41] John McCarthy,et al. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955 , 2006, AI Mag..
[42] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[43] Ilya Sharapov,et al. FastNorm: Improving Numerical Stability of Deep Network Training with Efficient Normalization , 2018 .
[44] Dinggang Shen,et al. A Robust Deep Model for Improved Classification of AD/MCI Patients , 2015, IEEE Journal of Biomedical and Health Informatics.
[45] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[46] Xavier Bresson,et al. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks , 2017, NIPS.
[47] Jieping Ye,et al. Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[48] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[49] S. Sitharama Iyengar,et al. Data-Driven Techniques in Disaster Information Management , 2017, ACM Comput. Surv..
[50] João Gama,et al. Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.
[51] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[52] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[53] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[54] Ruslan Salakhutdinov,et al. Learning Deep Generative Models , 2009 .
[55] Ben Glocker,et al. Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks , 2017, MICCAI.
[56] Dariu Gavrila,et al. Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Jure Leskovec,et al. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.
[58] Zheng Wang,et al. Multi-task Representation Learning for Travel Time Estimation , 2018, KDD.
[59] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.
[60] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[61] Matti Pietikäinen,et al. Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.
[62] Xiaogang Wang,et al. Medical image classification with convolutional neural network , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).
[63] Mei Wang,et al. Deep Face Recognition: A Survey , 2018, Neurocomputing.
[64] Ming Zhou,et al. Coooolll: A Deep Learning System for Twitter Sentiment Classification , 2014, *SEMEVAL.
[65] Ming Liu,et al. Vehicle Detection Based on Area and Proportion Prior with Faster-RCNN , 2020 .
[66] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[67] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[68] James M. Ferryman,et al. Evaluating deep semantic segmentation networks for object detection in maritime surveillance , 2018, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[69] Jure Leskovec,et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems , 2018, KDD.
[70] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[71] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[72] Stefanos Zafeiriou,et al. A survey on face detection in the wild: Past, present and future , 2015, Comput. Vis. Image Underst..
[73] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[74] Soon Ki Jung,et al. Unsupervised Adversarial Learning for Dynamic Background Modeling , 2020, IW-FCV.
[75] Stan Matwin,et al. TrajectoryNet: an embedded GPS trajectory representation for point-based classification using recurrent neural networks , 2017, CASCON.
[76] Rui Guo,et al. Partially-Sparse Restricted Boltzmann Machine for Background Modeling and Subtraction , 2013, 2013 12th International Conference on Machine Learning and Applications.
[77] Yi Yang,et al. Dynamic Background Learning through Deep Auto-encoder Networks , 2014, ACM Multimedia.
[78] Yi Li,et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.
[79] Yuxiao Dong,et al. DeepInf: Social Influence Prediction with Deep Learning , 2018, KDD.
[80] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[81] Borko Furht,et al. A neural network approach to bayesian background modeling for video object segmentation , 2006, VISAPP.
[82] DeepInf , 2018, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
[83] Thierry Bouwmans,et al. Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..
[84] Weiwei Sun,et al. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision , 2018, IJCAI.
[85] Trevor Darrell,et al. FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation , 2016, ArXiv.
[86] Zehang Sun,et al. On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[87] Gang Wang,et al. DAG-Recurrent Neural Networks for Scene Labeling , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[88] Xindong Wu,et al. Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[89] Soon Ki Jung,et al. Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation , 2018, Neural Networks.
[90] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[91] Hao Chen,et al. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks , 2016, IEEE Transactions on Medical Imaging.
[92] Max Welling,et al. Graph Convolutional Matrix Completion , 2017, ArXiv.
[93] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[94] Marti A. Hearst. Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..
[95] Daniel Jurafsky,et al. Building DNN acoustic models for large vocabulary speech recognition , 2014, Comput. Speech Lang..
[96] Regina Barzilay,et al. Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction , 2017, J. Chem. Inf. Model..
[97] Shu Yang,et al. Learning Urban Navigation via Value Iteration Network , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).
[98] Xuan Song,et al. Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.
[99] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[100] T. J. Stonham,et al. A system for counting people in video images using neural networks to identify the background scene , 1996, Pattern Recognit..
[101] Yang Song,et al. Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[102] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[103] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[104] Soon Ki Jung,et al. OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds , 2014, ACCV.
[105] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[106] Chaiyaphum Siripanpornchana,et al. Travel-time prediction with deep learning , 2016, 2016 IEEE Region 10 Conference (TENCON).
[107] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[108] Chao Zhang,et al. Trajectory clustering via deep representation learning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[109] Santanu Chaudhury,et al. Acquisition of multimedia ontology: an application in preservation of cultural heritage , 2012, International Journal of Multimedia Information Retrieval.
[110] Max Welling,et al. Attention, Learn to Solve Routing Problems! , 2018, ICLR.
[111] Navdeep Jaitly,et al. Pointer Networks , 2015, NIPS.
[112] Shimeng Yu,et al. Motion background modeling based on context-encoder , 2016, 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR).
[113] Iasonas Kokkinos,et al. Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs , 2016, ECCV.
[114] JavedSajid,et al. Decomposition into low-rank plus additive matrices for background/foreground separation , 2017 .
[115] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[116] Mahmoud Neji,et al. Moving Objects Segmentation Based on DeepSphere in Video Surveillance , 2019, ISVC.
[117] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[118] Thierry Bouwmans,et al. Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance , 2014, Comput. Vis. Image Underst..
[119] Qingcai Chen,et al. Fuzzy deep belief networks for semi-supervised sentiment classification , 2014, Neurocomputing.
[120] Pascal Vincent,et al. Artificial Neural Networks Applied to Taxi Destination Prediction , 2015, DC@PKDD/ECML.
[121] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[122] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[123] Geoffrey E. Hinton,et al. On deep generative models with applications to recognition , 2011, CVPR 2011.
[124] Guosheng Lin,et al. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[125] Xiaoxiao Li,et al. Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[126] Yadwinder Kaur,et al. Various Image Segmentation Techniques: A Review , 2014 .
[127] Suvrit Sra,et al. A Critical View of Global Optimality in Deep Learning , 2018, ArXiv.
[128] Lucia Maddalena,et al. A Self-organizing Approach to Detection of Moving Patterns for Real-Time Applications , 2007, BVAI.
[129] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[130] Pascal Vincent,et al. Disentangling Factors of Variation for Facial Expression Recognition , 2012, ECCV.
[131] Pieter Abbeel,et al. Value Iteration Networks , 2016, NIPS.
[132] Yoshua Bengio,et al. DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation , 2018, ArXiv.
[133] Guosheng Lin,et al. MoNet: Deep Motion Exploitation for Video Object Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[134] Jieping Ye,et al. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.
[135] Marcus Liwicki,et al. Scene labeling with LSTM recurrent neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[136] Mao Ye,et al. Motion detection via a couple of auto-encoder networks , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).
[137] Qing Li,et al. T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction , 2016, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).
[138] Le Song,et al. 2 Common Formulation for Greedy Algorithms on Graphs , 2018 .
[139] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[140] Kai Yu,et al. An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition , 2015, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP).
[141] Enhong Chen,et al. Temporally Adaptive Restricted Boltzmann Machine for Background Modeling , 2015, AAAI.
[142] René Vidal,et al. Global Optimality in Tensor Factorization, Deep Learning, and Beyond , 2015, ArXiv.
[143] Theodosios Pavlidis. Fundamentals of Picture Segmentation , 1977 .
[144] Limin Luo,et al. Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks , 2021, IEEE Transactions on Industrial Electronics.
[145] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[146] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[147] Soon Ki Jung,et al. Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset , 2015, Comput. Sci. Rev..
[148] Xiaogang Wang,et al. Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[149] Zhuowen Tu,et al. Image Parsing: Unifying Segmentation, Detection, and Recognition , 2005, International Journal of Computer Vision.
[150] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[151] Raman Arora,et al. On the Implicit Bias of Dropout , 2018, ICML.
[152] Jun-Sik Kim,et al. Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[153] Raymond Veldhuis,et al. A survey of face recognition techniques under occlusion , 2020, IET Biom..
[154] Vibha Vyas,et al. Review of Vehicle Detection Systems in Advanced Driver Assistant Systems , 2019, Archives of Computational Methods in Engineering.
[155] Alexander G. Schwing,et al. MaskRNN: Instance Level Video Object Segmentation , 2018, NIPS.
[156] Holger Schwenk,et al. Continuous Space Translation Models for Phrase-Based Statistical Machine Translation , 2012, COLING.
[157] Eldad Haber,et al. Stable architectures for deep neural networks , 2017, ArXiv.
[158] Honglak Lee,et al. Learning hierarchical representations for face verification with convolutional deep belief networks , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[159] Liu Jian. Learning Technique of Probabilistic Graphical Models: a Review , 2014 .
[160] Gang Zeng,et al. Fast Semantic Image Segmentation with High Order Context and Guided Filtering , 2016, ArXiv.
[161] Dimitrios Gunopulos,et al. Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.
[162] Qi Wang,et al. ACM: Adaptive Cross-Modal Graph Convolutional Neural Networks for RGB-D Scene Recognition , 2019, AAAI.
[163] Guillermo Sapiro,et al. On the Stability of Deep Networks , 2014, ICLR.
[164] Jiss Kuruvilla,et al. A review on image processing and image segmentation , 2016, 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE).
[165] Philip S. Yu,et al. Deep Learning for Spatio-Temporal Data Mining: A Survey , 2019, IEEE Transactions on Knowledge and Data Engineering.
[166] Paramartha Dutta,et al. A Review of Object Detection Models based on Convolutional Neural Network , 2019, Advances in Intelligent Systems and Computing.
[167] René Vidal,et al. Global Optimality in Neural Network Training , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[168] Garrison W. Cottrell,et al. Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[169] Pietro Perona,et al. Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[170] Sven Behnke,et al. Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks , 2014, KI.
[171] Mario Ignacio Chacon Murguia,et al. Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios , 2015, Pattern Recognit..
[172] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[173] Mubarak Shah,et al. Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network , 2017, ArXiv.
[174] Eldad Haber,et al. Reversible Architectures for Arbitrarily Deep Residual Neural Networks , 2017, AAAI.
[175] Youyong Kong,et al. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification , 2017, IEEE Transactions on Fuzzy Systems.
[176] Ben Glocker,et al. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease , 2018, Medical Image Anal..
[177] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[178] Shahrokh Valaee,et al. Ising-dropout: A Regularization Method for Training and Compression of Deep Neural Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[179] Jayadeep Sasikumar,et al. Investigating the Application of Deep Convolutional Neural Networks in Semi-supervised Video Object Segmentation , 2018 .
[180] Camille Couprie,et al. Semantic Segmentation using Adversarial Networks , 2016, NIPS 2016.
[181] Mee Hong Ling,et al. A Survey on Reinforcement Learning Models and Algorithms for Traffic Signal Control , 2017, ACM Comput. Surv..
[182] Frédéric Jurie,et al. An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks , 2017, NIPS 2017.
[183] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[184] Fei-Yue Wang,et al. Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.
[185] Amir H. Behzadan,et al. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery , 2020, Adv. Eng. Informatics.
[186] Luc Van Gool,et al. One-Shot Video Object Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[187] Ben J. A. Kröse,et al. Efficient Greedy Learning of Gaussian Mixture Models , 2003, Neural Computation.
[188] Alfredo De Santis,et al. Network anomaly detection with the restricted Boltzmann machine , 2013, Neurocomputing.
[189] Jonathan Masci,et al. Geometric deep learning , 2016, SIGGRAPH ASIA Courses.
[190] Chang-Su Kim,et al. Online Video Object Segmentation via Convolutional Trident Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[191] Jure Leskovec,et al. Graph Structure of Neural Networks , 2020, ICML.
[192] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[193] Medhat Moussa,et al. Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends , 2020, IEEE Transactions on Intelligent Transportation Systems.
[194] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[195] Nicola De Cao,et al. MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.
[196] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[197] Yong Wang,et al. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.
[198] David Gerónimo Gómez,et al. Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.