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A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation (BP).
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How auto-encoders could provide credit assignment in deep networks via target propagation
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Auto-encoding variational Bayes
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Difference target propagation
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H. Makino and T. Komiyama · 2015
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Learning enhances sensory and multiple non-sensory representations in primary visual cortex
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Control of synaptic plasticity in deep cortical networks
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Generalization of equilibrium propagation to vector field dynamics
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Equivalence of equilibrium propagation and recurrent backpropagation
B. Scellier and Y. Bengio · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al · 2019
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Language models are few-shot learners
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex
J. M. Pakan, S. C. Lowe, E. Dylda, S. W. Keemink, S. P. Currie, C. A. Coutts, and N. L. Rochefort · 2016
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Mastering the game of Go with deep neural networks and tree search
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Mismatch receptive fields in mouse visual cortex
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Visuomotor coupling shapes the functional development of mouse visual cortex
A. Attinger, B. Wang, and G. B. Keller · 2017
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A tutorial on the free-energy framework for modelling perception and learning
R. Bogacz · 2017
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C. L. Buckley, C. S. Kim, S. McGregor, and A. K. Seth · 2017
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Analog circuits to accelerate the relaxation process in the equilibrium propagation algorithm
A. N. Foroushani, H. Assaf, F. H. Noshahr, Y. Savaria, and M. Sawan · 2020
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Training end-to-end analog neural networks with equilibrium propagation
J. Kendall, R. Pantone, K. Manickavasagam, Y. Bengio, and B. Scellier · 2020
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A theoretical framework for target propagation
A. Meulemans, F. Carzaniga, J. Suykens, J. Sacramento, and B. F. Grewe · 2020
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Predictive coding approximates backprop along arbitrary computation graphs
B. Millidge, A. Tschantz, and C. L. Buckley · 2020
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The neural coding framework for learning generative models
A. Ororbia and D. Kifer · 2020
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Brain computation by assemblies of neurons
C. H. Papadimitriou, S. S. Vempala, D. Mitropolsky, M. Collins, and W. Maass · 2020
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Memory devices and applications for in-memory computing
A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, and E. Eleftheriou · 2020
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Can the brain do backpropagation?—Exact implementation of backpropagation in predictive coding networks
Y. Song, T. Lukasiewicz, Z. Xu, and R. Bogacz · 2020
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Predictive coding: A theoretical and experimental review
B. Millidge, A. K. Seth, and C. L. Buckley · 2021
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Zero-shot text-to-image generation
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever · 2021
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Associative memories via predictive coding
T. Salvatori, Y. Song, Y. Hong, S. Frieder, L. Sha, Z. Xu, R. Bogacz, and T. Lukasiewicz · 2021
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Towards scaling difference target propagation by learning backprop targets
M. Ernoult, F. Normandin, A. Moudgil, S. Spinney, E. Belilovsky, I. Rish, B. A. Richards, and Y. Bengio · 2022
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Predictive coding: Towards a future of deep learning beyond backpropagation?
B. Millidge, T. Salvatori, Y. Song, R. Bogacz, and T. Lukasiewicz · 2022
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Inferring neural activity before plasticity: A foundation for learning beyond backpropagation
Y. Song, B. G. Millidge, T. Salvatori, T. Lukasiewicz, Z. Xu, and R. Bogacz · 2022
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