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Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks.
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Learning curves for stochastic gradient descent in linear feedforward networks
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2006
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Gradient learning in spiking neural networks by dynamic perturbation of conductances
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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How to do backpropagation in a brain
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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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Learning multiple layers of features from tiny images
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Theories of error back-propagation in the brain
James C.R. Whittington and Rafal Bogacz · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Deep learning without weight transport
Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, and Douglas B. Tweed · 2019
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Greedy layerwise learning can scale to imagenet
Eugene Belilovsky, Michael Eickenberg, and Edouard Oyallon · 2019
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Putting an end to end-to-end: Gradient-isolated learning of representations
Sindy Löwe, Peter O’Connor, and Bastiaan S. Veeling · 2019
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Training neural networks with local error signals
Arild Nøkland and Lars Hiller Eidnes · 2019
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How auto-encoders could provide credit assignment in deep networks via target propagation
Yoshua Bengio · 2014
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Biologically-plausible learning algorithms can scale to large datasets
Will Xiao, Honglin Chen, Qianli Liao, and Tomaso A. Poggio · 2019
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Decoupled greedy learning of cnns
Eugene Belilovsky, Michael Eickenberg, and Edouard Oyallon · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Interlocking backpropagation: Improving depthwise model-parallelism
Aidan N. Gomez, Oscar Key, Stephen Gou, Nick Frosst, Jeff Dean, and Yarin Gal · 2020
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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Parallel training of deep networks with local updates
Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, and Pieter Abbeel · 2020
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Backpropagation and the brain
Timothy P. Lillicrap, Adam Santoro, Luke Marris, Colin J. Akerman, and Geoffrey Hinton · 2020
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Loco: Local contrastive representation learning
Yuwen Xiong, Mengye Ren, and Raquel Urtasun · 2020
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Credit assignment through broadcasting a global error vector
David G. Clark, L. F. Abbott, and SueYeon Chung · 2021
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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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A rapid and efficient learning rule for biological neural circuits
Eren Sezener, Agnieszka Grabska-Barwińska, Dimitar Kostadinov, Maxime Beau, Sanjukta Krishnagopal, David Budden, Marcus Hutter, Joel Veness, Matthew Botvinick, Claudia Clopath, et al · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Gated linear networks
Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, et al · 2021
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Revisiting locally supervised learning: an alternative to end-to-end training
Yulin Wang, Zanlin Ni, Shiji Song, Le Yang, and Gao Huang · 2021
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Activation sharing with asymmetric paths solves weight transport problem without bidirectional connection
Sunghyeon Woo, Jeongwoo Park, Jiwoo Hong, and Dongsuk Jeon · 2021
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Gradients without backpropagation
Atilim Günes Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, and Philip H. S. Torr · 2022
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Local learning with neuron groups
Adeetya Patel, Michael Eickenberg, and Eugene Belilovsky · 2022
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Learning by directional gradient descent
David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, and Hado van Hasselt · 2022
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, and Colin J. Akerman · 2041
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