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An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming.
Polynomial-time approximation algorithms for the ising model
Mark Jerrum and Alistair Sinclair · 1993
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Restricted boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 2009
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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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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Exploration of the (non-) asymptotic bias and variance of stochastic gradient langevin dynamics
Sebastian J Vollmer, Konstantinos C Zygalakis, and Yee Whye Teh · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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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
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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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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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Global convergence of langevin dynamics based algorithms for nonconvex optimization
Pan Xu, Jinghui Chen, Difan Zou, and Quanquan Gu · 2018
Cited alongside, same era.
Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Training deep energy-based models with f-divergence minimization
Lantao Yu, Yang Song, Jiaming Song, and Stefano Ermon · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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A contrastive learning approach for training variational autoencoder priors
Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat · 2021
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Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2021
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Mggan: Solving mode collapse using manifold-guided training
Duhyeon Bang and Hyunjung Shim · 2021
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Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Cited alongside, same era.
Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2019
Cited alongside, same era.
Maximum entropy generators for energy-based models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio · 2019
Cited alongside, same era.
Learning non-convergent non-persistent short-run mcmc toward energy-based model
Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
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Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2021
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Learning energy-based models by diffusion recovery likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma · 2021
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No mcmc for me: Amortized sampling for fast and stable training of energy-based models
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, and David Duvenaud · 2021
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Masked autoencoders are scalable vision learners, 2021
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Training {gan}s with stronger augmentations via contrastive discriminator
Jongheon Jeong and Jinwoo Shin · 2021
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Rebooting acgan: Auxiliary classifier gans with stable training
Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Conjugate energy-based models
Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, and Jan-Willem van de Meent · 2021
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Vaebm: A symbiosis between variational autoencoders and energy-based models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2021
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Jem++: Improved techniques for training jem
Xiulong Yang and Shihao Ji · 2021
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Pseudo-spherical contrastive divergence
Lantao Yu, Jiaming Song, Yang Song, and Stefano Ermon · 2021
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Learning energy-based generative models via coarse-to-fine expanding and sampling
Yang Zhao, Jianwen Xie, and Ping Li · 2021
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Faster convergence of stochastic gradient Langevin dynamics for non-log-concave sampling
Difan Zou, Pan Xu, and Quanquan Gu · 2021
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Bi-level doubly variational learning for energy-based latent variable models
Ge Kan, Jinhu Lü, Tian Wang, Baochang Zhang, Aichun Zhu, Lei Huang, Guodong Guo, and Hichem Snoussi · 2022
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MCMC should mix: Learning energy-based model with neural transport latent space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, and Ying Nian Wu · 2022
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A tale of two flows: Cooperative learning of langevin flow and normalizing flow toward energy-based model
Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li · 2022
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