Are we ready for a new paradigm shift? A survey on visual deep MLP

[1]  Shuicheng Yan,et al.  Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Mingxing Tan,et al.  PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions , 2022, ICLR.

[3]  Jian Sun,et al.  Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Cuiling Lan,et al.  ActiveMLP: An MLP-like Architecture with Active Token Mixer , 2022, ArXiv.

[5]  Vishal M. Patel,et al.  UNeXt: MLP-based Rapid Medical Image Segmentation Network , 2022, MICCAI.

[6]  Pradeep Kumar Singh,et al.  GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron , 2022, Contrast media & molecular imaging.

[7]  ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond , 2022, 2202.10108.

[8]  Chengrou Lu,et al.  Visual attention network , 2022, Computational Visual Media.

[9]  Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework , 2022, ArXiv.

[10]  Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs , 2022, ArXiv.

[11]  Wenhao Jiang,et al.  DynaMixer: A Vision MLP Architecture with Dynamic Mixing , 2022, ICML.

[12]  Trevor Darrell,et al.  A ConvNet for the 2020s , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  P. Milanfar,et al.  MAXIM: Multi-Axis MLP for Image Processing , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Kai Han,et al.  PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture , 2022, ArXiv.

[15]  X. Zhang,et al.  RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Alan Yuille,et al.  Masked Feature Prediction for Self-Supervised Visual Pre-Training , 2021, ArXiv.

[17]  Chao Xu,et al.  An Image Patch is a Wave: Phase-Aware Vision MLP , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  François Rameau,et al.  PointMixer: MLP-Mixer for Point Cloud Understanding , 2021, ECCV.

[19]  Li Dong,et al.  Swin Transformer V2: Scaling Up Capacity and Resolution , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Han Hu,et al.  SimMIM: a Simple Framework for Masked Image Modeling , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Ross B. Girshick,et al.  Masked Autoencoders Are Scalable Vision Learners , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Guanglu Song,et al.  UniNet: Unified Architecture Search with Convolution, Transformer, and MLP , 2021, ECCV.

[23]  Chong Luo,et al.  Sparse MLP for Image Recognition: Is Self-Attention Really Necessary? , 2021, AAAI.

[24]  Kai Han,et al.  Hire-MLP: Vision MLP via Hierarchical Rearrangement , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Ping Luo,et al.  CycleMLP: A MLP-like Architecture for Dense Prediction , 2021, ICLR.

[26]  Kai Han,et al.  CMT: Convolutional Neural Networks Meet Vision Transformers , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Nenghai Yu,et al.  CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  P. Luo,et al.  PVT v2: Improved baselines with Pyramid Vision Transformer , 2021, Computational Visual Media.

[29]  Shuicheng Yan,et al.  VOLO: Vision Outlooker for Visual Recognition , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Yunfeng Cai,et al.  S2-MLP: Spatial-Shift MLP Architecture for Vision , 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

[31]  Jianmin Bao,et al.  Uformer: A General U-Shaped Transformer for Image Restoration , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Cho-Jui Hsieh,et al.  When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations , 2021, ICLR.

[33]  Jianfei Cai,et al.  Less is More: Pay Less Attention in Vision Transformers , 2021, AAAI.

[34]  Shi-Min Hu,et al.  Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Daguang Xu,et al.  UNETR: Transformers for 3D Medical Image Segmentation , 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

[36]  Fahad Shahbaz Khan,et al.  Transformers in Vision: A Survey , 2021, ACM Comput. Surv..

[37]  Yi Tay,et al.  Efficient Transformers: A Survey , 2020, ACM Comput. Surv..

[38]  Jun Yu,et al.  Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Mingchen Zhuge,et al.  Skating-Mixer: Multimodal MLP for Scoring Figure Skating , 2022 .

[40]  Yali Wang,et al.  MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video , 2021, ArXiv.

[41]  Lu Yuan,et al.  PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers , 2021, ArXiv.

[42]  Philipp Benz,et al.  Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs , 2021, BMVC.

[43]  Ross Wightman,et al.  ResNet strikes back: An improved training procedure in timm , 2021, ArXiv.

[44]  Ali Hassani,et al.  ConvMLP: Hierarchical Convolutional MLPs for Vision , 2021, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[45]  Luc Van Gool,et al.  SwinIR: Image Restoration Using Swin Transformer , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

[46]  Yunfeng Cai,et al.  S2-MLPv2: Improved Spatial-Shift MLP Architecture for Vision , 2021, ArXiv.

[47]  Shenghua Gao,et al.  AS-MLP: An Axial Shifted MLP Architecture for Vision , 2021, ICLR.

[48]  Yunfeng Cai,et al.  Rethinking Token-Mixing MLP for MLP-based Vision Backbone , 2021, BMVC.

[49]  Adriana Kovashka,et al.  Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers , 2021, ArXiv.

[50]  Furu Wei,et al.  BEiT: BERT Pre-Training of Image Transformers , 2021, ArXiv.

[51]  Luc Van Gool,et al.  Video Super-Resolution Transformer , 2021, ArXiv.

[52]  Carlos Riquelme,et al.  Scaling Vision with Sparse Mixture of Experts , 2021, NeurIPS.

[53]  Quoc V. Le,et al.  CoAtNet: Marrying Convolution and Attention for All Data Sizes , 2021, NeurIPS.

[54]  Wassim Hamidouche,et al.  Reveal of Vision Transformers Robustness against Adversarial Attacks , 2021, ArXiv.

[55]  Zilong Huang,et al.  Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer , 2021, ArXiv.

[56]  Roozbeh Mottaghi,et al.  Container: Context Aggregation Network , 2021, NeurIPS.

[57]  Ralph R. Martin,et al.  Can Attention Enable MLPs To Catch Up With CNNs? , 2021, Comput. Vis. Media.

[58]  Manuel Ladron de Guevara,et al.  MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation , 2021, ArXiv.

[59]  Fahad Shahbaz Khan,et al.  Intriguing Properties of Vision Transformers , 2021, NeurIPS.

[60]  Quoc V. Le,et al.  Pay Attention to MLPs , 2021, NeurIPS.

[61]  Matthieu Cord,et al.  ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[62]  Luke Melas-Kyriazi,et al.  Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet , 2021, ArXiv.

[63]  A. Dosovitskiy,et al.  MLP-Mixer: An all-MLP Architecture for Vision , 2021, NeurIPS.

[64]  Chunhua Shen,et al.  Twins: Revisiting the Design of Spatial Attention in Vision Transformers , 2021, NeurIPS.

[65]  Saining Xie,et al.  An Empirical Study of Training Self-Supervised Vision Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[66]  Quoc V. Le,et al.  EfficientNetV2: Smaller Models and Faster Training , 2021, ICML.

[67]  Marten van Dijk,et al.  On the Robustness of Vision Transformers to Adversarial Examples , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[68]  Matthieu Cord,et al.  Going deeper with Image Transformers , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[69]  Andreas Veit,et al.  Understanding Robustness of Transformers for Image Classification , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[70]  Enhua Wu,et al.  Transformer in Transformer , 2021, NeurIPS.

[71]  Xiang Li,et al.  Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[72]  Vishal M. Patel,et al.  Medical Transformer: Gated Axial-Attention for Medical Image Segmentation , 2021, MICCAI.

[73]  Francis E. H. Tay,et al.  Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[74]  Matthieu Cord,et al.  Training data-efficient image transformers & distillation through attention , 2020, ICML.

[75]  Ralph R. Martin,et al.  PCT: Point cloud transformer , 2020, Computational Visual Media.

[76]  Wen Gao,et al.  Pre-Trained Image Processing Transformer , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[77]  Klaus Dietmayer,et al.  Point Transformer , 2020, IEEE Access.

[78]  S. Gelly,et al.  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.

[79]  Quoc V. Le,et al.  Meta Pseudo Labels , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[80]  Masato Taki,et al.  RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision? , 2021, ArXiv.

[81]  Stephen Lin,et al.  Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[82]  Zangwei Zheng,et al.  Sparse-MLP: A Fully-MLP Architecture with Conditional Computation , 2021, ArXiv.

[83]  Long Zhao,et al.  Aggregating Nested Transformers , 2021, ArXiv.

[84]  Shi-Min Hu,et al.  Jittor: a novel deep learning framework with meta-operators and unified graph execution , 2020, Science China Information Sciences.

[85]  Fillia Makedon,et al.  A Survey on Contrastive Self-supervised Learning , 2020, Technologies.

[86]  Geoffrey E. Hinton,et al.  Big Self-Supervised Models are Strong Semi-Supervised Learners , 2020, NeurIPS.

[87]  Nicolas Usunier,et al.  End-to-End Object Detection with Transformers , 2020, ECCV.

[88]  Chongruo Wu,et al.  ResNeSt: Split-Attention Networks , 2020, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[89]  Kaiming He,et al.  Designing Network Design Spaces , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  Kaiming He,et al.  Improved Baselines with Momentum Contrastive Learning , 2020, ArXiv.

[91]  Geoffrey E. Hinton,et al.  A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.

[92]  Ankesh Anand Contrastive Self-Supervised Learning , 2020 .

[93]  S. Gelly,et al.  Big Transfer (BiT): General Visual Representation Learning , 2019, ECCV.

[94]  Ross B. Girshick,et al.  Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[95]  Asifullah Khan,et al.  A survey of the recent architectures of deep convolutional neural networks , 2019, Artificial Intelligence Review.

[96]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[97]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[98]  Kai Chen,et al.  MMDetection: Open MMLab Detection Toolbox and Benchmark , 2019, ArXiv.

[99]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[100]  Quoc V. Le,et al.  Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[101]  Quoc V. Le,et al.  NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[102]  Kaiming He,et al.  Panoptic Feature Pyramid Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[103]  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).

[104]  Matthias Bethge,et al.  ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.

[105]  Thomas G. Dietterich,et al.  Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.

[106]  Ning Xu,et al.  Wide Activation for Efficient and Accurate Image Super-Resolution , 2018, ArXiv.

[107]  Yuning Jiang,et al.  Unified Perceptual Parsing for Scene Understanding , 2018, ECCV.

[108]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[109]  Jun Yu,et al.  Local Deep-Feature Alignment for Unsupervised Dimension Reduction , 2018, IEEE Transactions on Image Processing.

[110]  Yu-Sheng Chen,et al.  Learning Deep Convolutional Networks for Demosaicing , 2018, ArXiv.

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

[112]  Garrison W. Cottrell,et al.  Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[113]  Frank Hutter,et al.  Fixing Weight Decay Regularization in Adam , 2017, ArXiv.

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

[115]  Bolei Zhou,et al.  Scene Parsing through ADE20K Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[117]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[118]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[119]  Andrew Zisserman,et al.  Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[120]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[121]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[122]  Geoffrey E. Hinton,et al.  Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.

[123]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[124]  Tae Hyun Kim,et al.  Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[125]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[126]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[128]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[129]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[130]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[131]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

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

[133]  Kevin Gimpel,et al.  Gaussian Error Linear Units (GELUs) , 2016 .

[134]  Kevin Gimpel,et al.  Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units , 2016, ArXiv.

[135]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[137]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[138]  Seyed-Mohsen Moosavi-Dezfooli,et al.  DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[139]  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.

[140]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[141]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[142]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[143]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[145]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[146]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[147]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

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

[149]  Léon Bottou,et al.  Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.

[150]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[151]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[153]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[154]  Erik Lindholm,et al.  NVIDIA Tesla: A Unified Graphics and Computing Architecture , 2008, IEEE Micro.

[155]  Jürgen Schmidhuber,et al.  New Millennium AI and the Convergence of History: Update of 2012 , 2012 .

[156]  Tom M. Mitchell,et al.  The Need for Biases in Learning Generalizations , 2007 .

[157]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[158]  T. Poggio,et al.  Networks and the best approximation property , 1990, Biological Cybernetics.

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

[160]  Toshiyuki TANAKA Mean-field theory of Boltzmann machine learning , 1998 .

[161]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

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

[163]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[164]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

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

[166]  H. White,et al.  Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.

[167]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[168]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[169]  Geoffrey E. Hinton Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space , 1989, Neural Computation.

[170]  Eric B. Baum,et al.  On the capabilities of multilayer perceptrons , 1988, J. Complex..

[171]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[172]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[173]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[174]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[175]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[176]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[177]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[178]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[179]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[180]  C. Sherrington Observations on the scratch‐reflex in the spinal dog , 1906, The Journal of physiology.