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Local learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Competitive learning: From interactive activation to adaptive resonance
Stephen Grossberg · 1987
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Associative recall of memory without errors
Ido Kanter and Haim Sompolinsky · 1987
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Efficient backprop
Yann LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 1998
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
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Learning and inference in the brain
Karl Friston · 2003
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A theory of cortical responses
Karl Friston · 2005
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How auto-encoders could provide credit assignment in deep networks via target propagation
Yoshua Bengio · 2014
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Distributed optimization of deeply nested systems
Miguel Carreira-Perpiñán and Weiran Wang · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Difference target propagation
Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, and Yoshua Bengio · 2015
Earlier work this paper cites.
Optimizing neural networks with Kronecker-factored approximate curvature
James Martens and Roger Grosse · 2015
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Training neural networks without gradients: A scalable admm approach
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, and Tom Goldstein · 2016
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Benjamin Scellier and Yoshua Bengio · 2017
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An approximation of the error backpropagation algorithm in a predictive coding network with local Hebbian synaptic plasticity
James CR Whittington and Rafal Bogacz · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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A mean field view of the landscape of two-layer neural networks
Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Deep learning without weight transport
Mohamed Akrout, Collin Wilson, Peter Humphreys, Timothy Lillicrap, and Douglas B Tweed · 2019
Earlier work this paper cites.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Cited alongside, same era.
Beyond backprop: Online alternating minimization with auxiliary variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, et al · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Cited alongside, same era.
Statistical mechanics of deep learning
Yasaman Bahri, Jonathan Kadmon, Jeffrey Pennington, Sam S Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli · 2020
Cited alongside, same era.
Deriving differential target propagation from iterating approximate inverses
Y Bengio · 2020
Cited alongside, same era.
Towards scaling difference target propagation by learning backprop targets
Maxence M Ernoult, Fabrice Normandin, Abhinav Moudgil, Sean Spinney, Eugene Belilovsky, Irina Rish, Blake Richards, and Yoshua Bengio · 2022
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On the relationship between predictive coding and backpropagation
Robert Rosenbaum · 2022
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Understanding predictive coding as a second-order trust-region method
Francesco Innocenti, Ryan Singh, and Christopher Buckley · 2023
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Backpropagation at the infinitesimal inference limit of energy-based models: Unifying predictive coding, equilibrium propagation, and contrastive hebbian learning
Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, and Rafal Bogacz · 2023
Later among the works it cites.
Scaling forward gradient with local losses
Mengye Ren, Simon Kornblith, Renjie Liao, and Geoffrey Hinton · 2023
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Mario Geiger, Leonardo Petrini, and Matthieu Wyart · 2020
Cited alongside, same era.
Understanding approximate fisher information for fast convergence of natural gradient descent in wide neural networks
Ryo Karakida and Kazuki Osawa · 2020
Cited alongside, same era.
Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
Cited alongside, same era.
A theoretical framework for target propagation
Alexander Meulemans, Francesco Carzaniga, Johan Suykens, João Sacramento, and Benjamin F Grewe · 2020
Cited alongside, same era.
Can the brain do backpropagation?—exact implementation of backpropagation in predictive coding networks
Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, and Rafal Bogacz · 2020
Cited alongside, same era.
Tensor programs II: Neural tangent kernel for any architecture
Greg Yang · 2020
Cited alongside, same era.
Deep learning: a statistical viewpoint
Peter L Bartlett, Andrea Montanari, and Alexander Rakhlin · 2021
Cited alongside, same era.
Tommaso Salvatori, Ankur Mali, Christopher L Buckley, Thomas Lukasiewicz, Rajesh PN Rao, Karl Friston, and Alexander Ororbia · 2023
Later among the works it cites.
Feature-learning networks are consistent across widths at realistic scales
Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, and Cengiz Pehlevan · 2023
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Tensor programs IVb: Adaptive optimization in the infinite-width limit
Greg Yang and Etai Littwin · 2023
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Understanding and improving optimization in predictive coding networks
Nicholas Alonso, Jeffrey Krichmar, and Emre Neftci · 2024
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How feature learning can improve neural scaling laws
Blake Bordelon, Alexander Atanasov, and Cengiz Pehlevan · 2024
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Formalizing locality for normative synaptic plasticity models
Colin Bredenberg, Ezekiel Williams, Cristina Savin, Blake Richards, and Guillaume Lajoie · 2024
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Scaling exponents across parameterizations and optimizers
Katie E Everett, Lechao Xiao, Mitchell Wortsman, Alexander A Alemi, Roman Novak, Peter J Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, and Jeffrey Pennington · 2024
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Only strict saddles in the energy landscape of predictive coding networks?
Francesco Innocenti, El Mehdi Achour, Ryan Singh, and Christopher L Buckley · 2024
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On the parameterization of second-order optimization effective towards the infinite width
Satoki Ishikawa and Ryo Karakida · 2024
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Super consistency of neural network landscapes and learning rate transfer
Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, and Antonio Orvieto · 2024
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Benchmarking predictive coding networks–made simple
Luca Pinchetti, Chang Qi, Oleh Lokshyn, Gaspard Olivers, Cornelius Emde, Mufeng Tang, Amine M’Charrak, Simon Frieder, Bayar Menzat, Rafal Bogacz, et al · 2024
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Compute better spent: Replacing dense layers with structured matrices
Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, and Andrew Gordon Wilson · 2024
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Feature learning in infinite-depth neural networks
Greg Yang, Dingli Yu, Chen Zhu, and Soufiane Hayou · 2024
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