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Convolutional networks are ubiquitous in deep learning.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
David H Hubel and Torsten N Wiesel · 1962
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Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
Kunihiko Fukushima and Sei Miyake · 1982
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Competitive learning: From interactive activation to adaptive resonance
Stephen Grossberg · 1987
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Learning invariance from transformation sequences
Peter Földiák · 1991
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Learning invariant responses to the natural transformations of objects
Guy Wallis, Edmund Rolls, and Peter Foldiak · 1993
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Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex
William H. Bosking, Ying Zhang, Brett Schofield, and David Fitzpatrick · 1997
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Hierarchical models of object recognition in cortex
Maximilian Riesenhuber and Tomaso A. Poggio · 1999
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J Sejnowski · 2002
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Sleep-dependent plasticity requires cortical activity
Sushil K Jha, Brian E. Jones, Tammi Coleman, Nick Steinmetz, Chi-Tat Law, Gerald D. Griffin, Joshua D. Hawk, Nooreen Dabbish, Valery A. Kalatsky, and Marcos G. Frank · 2005
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A functional and perceptual signature of the second visual area in primates
Jeremy Freeman, Corey M. Ziemba, David J. Heeger, Eero P. Simoncelli, and J. Anthony Movshon · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Convolutional neural networks for speech recognition
Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
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Deep supervised, but not unsupervised, models may explain it cortical representation
Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte · 2014
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 2015
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Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance
Najib J. Majaj, Ha Hong, Ethan A. Solomon, and James J. DiCarlo · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Very deep convolutional networks for text classification
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun · 2016
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Direct feedback alignment provides learning in deep neural networks
Arild Nøkland · 2016
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Sgd: General analysis and improved rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik · 2019
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Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay · 2020
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Integrative benchmarking to advance neurally mechanistic models of human intelligence
Martin Schrimpf, Jonas Kubilius, Michael J Lee, N Apurva Ratan Murty, Robert Ajemian, and James J DiCarlo · 2020
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Scaling equilibrium propagation to deep convnets by drastically reducing its gradient estimator bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Julie Grollier, and Damien Querlioz · 2020
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Lstm fully convolutional networks for time series classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Shun Chen · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Linking network activity to synaptic plasticity during sleep: hypotheses and recent data
Carlos Puentes-Mestril and Sara J Aton · 2017
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Brain-score: Which artificial neural network for object recognition is most brain-like?
Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, and James J. DiCarlo · 2018
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018
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Feedback alignment in deep convolutional networks
Theodore H Moskovitz, Ashok Litwin-Kumar, and LF Abbott · 2018
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Deep supervised learning using local errors
Hesham Mostafa, Vishwajith Ramesh, and Gert Cauwenberghs · 2018
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Roman Pogodin and Peter E Latham · 2020
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Towards learning convolutions from scratch
Behnam Neyshabur · 2020
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Revisiting spatial invariance with low-rank local connectivity
Gamaleldin Elsayed, Prajit Ramachandran, Jonathon Shlens, and Simon Kornblith · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
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Learning in the machine: To share or not to share?
Jordan Ott, Erik J. Linstead, Nicholas LaHaye, and Pierre Baldi · 2020
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Mlp-mixer: An all-mlp architecture for vision, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Resmlp: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek, and Hervé Jégou · 2021
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Hanxiao Liu, Zihang Dai, David R So, and Quoc V Le · 2021
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Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior
Tiago Marques, Martin Schrimpf, and James J DiCarlo · 2021
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