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Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation.
Maximum likelihood from incomplete data via the em algorithm
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On the computational architecture of the neocortex: Ii the role of cortico-cortical loops
David Mumford · 1992
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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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Variational algorithms for approximate Bayesian inference
Matthew James Beal · 2003
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A theory of cortical responses
Karl Friston · 2005
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Variational free energy and the laplace approximation
Karl Friston, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will Penny · 2007
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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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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The free energy principle for action and perception: A mathematical review
Christopher L Buckley, Chang Sub Kim, Simon McGregor, and Anil K Seth · 2017
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A tutorial on the free-energy framework for modelling perception and learning
Rafal Bogacz · 2017
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Uci machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Decoupled weight decay regularization
I Loshchilov · 2017
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Deterministic variational inference for robust bayesian neural networks
Preventing deterioration of classification accuracy in predictive coding networks
Paul F Kinghorn, Beren Millidge, and Christopher L Buckley · 2022
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The neural coding framework for learning generative models
Alexander Ororbia and Daniel Kifer · 2022
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A primer on variational laplace (vl)
Peter Zeidman, Karl Friston, and Thomas Parr · 2023
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Sample as you infer: Predictive coding with langevin dynamics
Umais Zahid, Qinghai Guo, and Zafeirios Fountas · 2023
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Inferring neural activity before plasticity as a foundation for learning beyond backpropagation
Yuhang Song, Beren Millidge, Tommaso Salvatori, Thomas Lukasiewicz, Zhenghua Xu, and Rafal Bogacz · 2024
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Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E Turner, Jose Miguel Hernandez-Lobato, and Alexander L Gaunt · 2018
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.
Predictive coding: a theoretical and experimental review
Beren Millidge, Anil Seth, and Christopher L Buckley · 2021
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Associative memories via predictive coding
Tommaso Salvatori, Yuhang Song, Yujian Hong, Lei Sha, Simon Frieder, Zhenghua Xu, Rafal Bogacz, and Thomas Lukasiewicz · 2021
Cited alongside, same era.
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
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Reverse differentiation via predictive coding
Tommaso Salvatori, Yuhang Song, Zhenghua Xu, Thomas Lukasiewicz, and Rafal Bogacz · 2022
Cited alongside, same era.
Predictive coding approximates backprop along arbitrary computation graphs
Beren Millidge, Alexander Tschantz, and Christopher L Buckley
Cited in the paper.
Francesco Innocenti, El Mehdi Achour, Ryan Singh, and Christopher L Buckley · 2024
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Position: Bayesian deep learning is needed in the age of large-scale ai
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, et al · 2024
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Gradient-free variational learning with conditional mixture networks
Conor Heins, Hao Wu, Dimitrije Markovic, Alexander Tschantz, Jeff Beck, and Christopher Buckley · 2024
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Learning probability distributions of sensory inputs with monte carlo predictive coding
Gaspard Oliviers, Rafal Bogacz, and Alexander Meulemans · 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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