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Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm.
Maximum entropy generators for energy-based models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio · 1901
Earlier work this paper cites.
Videoflow: A flow-based generative model for video
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 1903
Earlier work this paper cites.
On the theory of Brownian motion
Paul Langevin · 1908
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Inference from iterative simulation using multiple sequences
Andrew Gelman, Donald B Rubin, et al · 1992
Earlier work this paper cites.
General methods for monitoring convergence of iterative simulations
Stephen P Brooks and Andrew Gelman · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Whole-sentence exponential language models: a vehicle for linguistic-statistical integration
Ronald Rosenfeld, Stanley F Chen, and Xiaojin Zhu · 2001
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Learning generative models via discriminative approaches
Zhuowen Tu · 2007
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A tutorial on adaptive mcmc
Christophe Andrieu and Johannes Thoms · 2008
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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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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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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MCMC using hamiltonian dynamics
Radford M Neal · 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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Learning deep energy models
Jiquan Ngiam, Zhenghao Chen, Pang W Koh, and Andrew Y Ng · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Better mixing via deep representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, and Salah Rifai · 2013
Earlier work this paper cites.
Optimal tuning of the hybrid monte carlo algorithm
Alexandros Beskos, Natesh Pillai, Gareth Roberts, Jesus-Maria Sanz-Serna, Andrew Stuart, et al · 2013
Earlier work this paper cites.
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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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Diederik Kingma and Max Welling · 2014
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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Learning generative convnets via multi-grid modeling and sampling
Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, and Ying Nian Wu · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Betterncourt, Ilya Sutskever, and David Duvenaud · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine · 2016
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Deep directed generative models with energy-based probability estimation
Taesup Kim and Yoshua Bengio · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
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Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
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Learning neural random fields with inclusive auxiliary generators
Yunfu Song and Zhijian Ou · 2018
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Revisiting the gelman-rubin diagnostic
Dootika Vats and Christina Knudson · 2018
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Learning neural trans-dimensional random field language models with noise-contrastive estimation
Bin Wang and Zhijian Ou · 2018
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Cooperative training of descriptor and generator networks
Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu · 2018
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
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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 · 2019
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Divergence triangle for joint training of generator model, energy-based model, and inferential model
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
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Building a telescope to look into high-dimensional image spaces
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Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport
Matthew Hoffman, Pavel Sountsov, Joshua V Dillon, Ian Langmore, Dustin Tran, and Srinivas Vasudevan · 2019
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Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik P Kingma, and Aapo Hyvärinen · 2019
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On learning non-convergent short-run mcmc toward energy-based model
Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2019
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Discrete flows: Invertible generative models of discrete data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, and Ben Poole · 2019
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Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2020
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No mcmc for me: Amortized sampling for fast and stable training of energy-based models
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Maximum entropy methods for texture synthesis: theory and practice
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