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Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty.
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 1903
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Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 1910
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A note on importance sampling using standardized weights
Augustine Kong · 1992
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Comments on “representations of knowledge in complex systems” by u. grenander and mi miller
JE Besag · 1994
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Probabilistic principal component analysis
Michael E Tipping and Christopher M Bishop · 1999
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Efficient learning of generative models via finite-difference score matching
Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, and Jun Zhu · 2007
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Estimating the hessian by back-propagating curvature
James Martens, Ilya Sutskever, and Kevin Swersky · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Hierarchical variational models
Rajesh Ranganath, Dustin Tran, and David Blei · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Analysis of k-nearest neighbor distances with application to entropy estimation
Shashank Singh and Barnabás Póczos · 2016
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Later among the works it cites.
Learning protein structure with a differentiable simulator
John Ingraham, Adam J Riesselman, Chris Sander, and Debora S Marks · 2019
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Maximum entropy generators for energy-based models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio · 2019
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Annealed denoising score matching: Learning energy-based models in high-dimensional spaces
Zengyi Li, Yubei Chen, and Friedrich T Sommer · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
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Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Calibrating energy-based generative adversarial networks
Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, and Aaron Courville · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Conditional noise-contrastive estimation of unnormalised models
Ciwan Ceylan and Michael U Gutmann · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
Cited alongside, same era.
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Unbiased implicit variational inference
Michalis K Titsias and Francisco Ruiz · 2019
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Your gan is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Energy-based models for atomic-resolution protein conformations
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives · 2020
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Flow contrastive estimation of energy-based models
Ruiqi Gao, Erik Nijkamp, Diederik P Kingma, Zhen Xu, Andrew M Dai, and Ying Nian Wu · 2020
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Learning the stein discrepancy for training and evaluating energy-based models without sampling
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, and Richard Zemel · 2020
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Stochastic security: Adversarial defense using long-run dynamics of energy-based models
Mitch Hill, Jonathan Mitchell, and Song-Chun Zhu · 2020
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Ar-dae: Towards unbiased neural entropy gradient estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal, and Chin-Wei Huang · 2020
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Hybrid discriminative-generative training via contrastive learning
Hao Liu and Pieter Abbeel · 2020
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Formal limitations on the measurement of mutual information
David McAllester and Karl Stratos · 2020
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Telescoping density-ratio estimation
Benjamin Rhodes, Kai Xu, and Michael U Gutmann · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Sliced score matching: A scalable approach to density and score estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2020
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