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We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y).
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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On learning non-convergent short-run mcmc toward energy-based model
Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 1904
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Computer vision with a single (robust) classifier
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry · 1906
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2006
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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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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
Regularized estimation of image statistics by score matching
Durk P Kingma and Yann Lecun · 2010
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Sergey Zagoruyko and Nikos Komodakis · 2014
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Cited alongside, same era.
Conditional noise-contrastive estimation of unnormalised models
Ciwan Ceylan and Michael U Gutmann · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Are generative classifiers more robust to adversarial attacks?
Yingzhen Li, John Bradshaw, and Yash Sharma · 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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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 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.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Foolbox: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
Cited alongside, same era.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
Cited alongside, same era.
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Towards the first adversarially robust neural network model on mnist
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2018
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Adversarial vulnerability of neural networks increases with input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, and David Lopez-Paz · 2018
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Learning neural random fields with inclusive auxiliary generators
Yunfu Song and Zhijian Ou · 2018
Later among the works it cites.
Adversarial distillation of bayesian neural network posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, and Richard Zemel · 2018
Later among the works it cites.
Residual flows for invertible generative modeling
Ricky TQ Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
Closest in time.
Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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Conditional generative models are not robust
Ethan Fetaya, Jörn-Henrik Jacobsen, and Richard Zemel · 2019
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Detecting out-of-distribution inputs to deep generative models using a test for typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, and Sebastien Bubeck · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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