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We present a novel algorithm for parameter learning in generic deep generative models that builds upon the predictive coding (PC) framework of computational neuroscience.
MCMC using Hamiltonian dynamics
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Is There an Analog of Nesterov Acceleration for MCMC?, October 2019
Yi-An Ma, Niladri Chatterji, Xiang Cheng, Nicolas Flammarion, Peter Bartlett, and Michael I. Jordan · 1902
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An Invariant Form for the Prior Probability in Estimation Problems
Harold Jeffreys · 1946
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Fluctuations of Resting Neural Membrane Potential
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Brownian dynamics as smart Monte Carlo simulation
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Divergence measures based on the Shannon entropy
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Besage, J. E · 1994
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Exponential convergence of Langevin distributions and their discrete approximations
Gareth O. Roberts and Richard L. Tweedie · 1996
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The Variational Formulation of the Fokker–Planck Equation
Richard Jordan, David Kinderlehrer, and Felix Otto · 1998
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Optimal scaling of discrete approximations to Langevin diffusions
Gareth O. Roberts and Jeffrey S. Rosenthal · 1998
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The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding
Michael N. Shadlen and William T. Newsome · 1998
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh P. N. Rao and Dana H. Ballard · 1999
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Reliable Fidelity and Diversity Metrics for Generative Models, February 2020
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2002
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Langevin Diffusions and Metropolis-Hastings Algorithms
G. O. Roberts and O. Stramer · 2002
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Learning and inference in the brain
Karl Friston · 2003
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Visualization of learning in multilayer perceptron networks using principal component analysis
M. Gallagher and T. Downs · 2003
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A theory of cortical responses
Karl Friston · 2005
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Dynamic predictive coding by the retina
Toshihiko Hosoya, Stephen A. Baccus, and Markus Meister · 2005
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Pattern recognition and machine learning
Christopher M. Bishop · 2006
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Predictive Coding Approximates Backprop along Arbitrary Computation Graphs
Beren Millidge, Alexander Tschantz, and Christopher L. Buckley · 2006
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NVAE: A Deep Hierarchical Variational Autoencoder, January 2021
Arash Vahdat and Jan Kautz · 2007
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Noise in the nervous system
A. Aldo Faisal, Luc P. J. Selen, and Daniel M. Wolpert · 2008
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Predictive coding under the free-energy principle
Karl Friston and Stefan Kiebel · 2008
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Predictive coding explains binocular rivalry: an epistemological review
Jakob Hohwy, Andreas Roepstorff, and Karl Friston · 2008
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Attention, Uncertainty, and Free-Energy
Harriet Feldman and Karl Friston · 2010
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2010
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Bayesian Data Analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin · 2015
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A tutorial on the free-energy framework for modelling perception and learning
Rafal Bogacz · 2015
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Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks, December 2015
Chunyuan Li, Changyou Chen, David Carlson, and Lawrence Carin · 2015
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Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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A Complete Recipe for Stochastic Gradient MCMC, October 2015
Yi-An Ma, Tianqi Chen, and Emily B. Fox · 2015
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Rewon Child · 2011
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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, November 2011
Matthew D. Hoffman and Andrew Gelman · 2011
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The benefits of noise in neural systems: bridging theory and experiment
Mark D. McDonnell and Lawrence M. Ward · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Handbook of Markov Chain Monte Carlo
Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng (ed.) · 2011
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
M. Welling and Y. Teh · 2011
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John Salvatier, Thomas Wiecki, and Christopher Fonnesbeck · 2015
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Stuck in a What? Adventures in Weight Space, February 2016
Zachary C. Lipton · 2016
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Neural Elements for Predictive Coding
Stewart Shipp · 2016
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Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo, March 2023b
Umais Zahid, Guo Qinghai, and Zafeirios Fountas · 2016
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, June 2017
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo
Matthew D. Hoffman · 2017
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Adam: A Method for Stochastic Optimization, January 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Visualizing the Loss Landscape of Neural Nets, December 2017
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2017
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Underdamped Langevin MCMC: A non-asymptotic analysis, January 2018
Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, and Michael I. Jordan · 2018
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Does predictive coding have a future?
Karl Friston · 2018
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Andre Wibisono · 2018
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Convergence Diagnostics for Markov Chain Monte Carlo
Vivekananda Roy · 2020
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A Predictive Processing Model of Episodic Memory and Time Perception
Zafeirios Fountas, Anastasia Sylaidi, Kyriacos Nikiforou, Anil K. Seth, Murray Shanahan, and Warrick Roseboom · 2022
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Efficient-VDVAE: Less is more, April 2022
Louay Hazami, Rayhane Mama, and Ragavan Thurairatnam · 2022
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Langevin Autoencoders for Learning Deep Latent Variable Models, September 2022
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, and Yutaka Matsuo · 2022
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