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We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks.
Generalizing the singular value decomposition
Charles F. van Loan · 1976
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Towards a generalized singular value decomposition
C. C. Paige and M. A. Saunders · 1981
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Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
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Competitive learning: from interactive activation to adaptive resonance
Stephen Grossberg · 1987
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The recent excitement about neural networks
Francis Crick · 1989
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A First Course in the Numerical Analysis of Differential Equations
Arieh Iserles · 1996
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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 · 2016
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Direct feedback alignment provides learning in deep neural networks
A. Nøkland · 2016
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Explaining the learning dynamics of direct feedback alignment
J Gilmer, C. Raffel, S. S. Schoenholz, M. Raghu, and J. Sohl-Dickstein · 2017
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A convergence analysis of gradient descent for deep linear neural networks
Sanjeev Arora, Nadav Cohen, Noah Golowich, and Wei Hu · 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 L.F. Abbott · 2018
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The implicit bias of depth: How incremental learning drives generalization
Daniel Gissin, Shai Shalev-Shwartz, and Amit Daniely · 2019
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Principled training of neural networks with direct feedback alignment
Julien Launay, Iacopo Poli, and Florent Krzakala · 2019
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Training DNNs in O(1) memory with MEM-DFA using random matrices
Tien Chu, Kamil Mykitiuk, Miron Szewczyk, Adam Wiktor, and Zbigniew Wojna · 2020
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Direct feedback alignment scales to modern deep learning tasks and architectures
Julien Launay, Iacopo Poli, François Boniface, and Florent Krzakala · 2020
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Spike-Train level Direct Feedback Alignment: Sidestepping backpropagation for on-chip training of spiking neural nets
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A mathematical theory of semantic development in deep neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2018
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Implicit regularization of discrete gradient dynamics in linear neural networks
Gauthier Gidel, Francis Bach, and Simon Lacoste-Julien · 2019
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J. Lee, R. Zhang, W. Zhang, Y. Liu, and P. Li · 2020
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The dynamics of learning with feedback alignment
Maria Refinetti, Stéphane d’Ascoli, Ruben Ohana, and Sebastian Goldt · 2020
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Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Charlotte Frenkel, Martin Lefebvre, and David Bol · 2021
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