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Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data

Jul 19, 2021
Jacob Kelly, Richard Zemel, Will Grathwohl

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Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Feb 08, 2021
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

* Energy-Based Models, Deep generative models, MCMC sampling 

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No MCMC for me: Amortized sampling for fast and stable training of energy-based models

Oct 14, 2020
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

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Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling

Feb 14, 2020
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel

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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

Dec 11, 2019
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky

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FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Oct 22, 2018
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

* 8 Pages, 6 figures 

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Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Feb 23, 2018
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud

* Published at ICLR 2018 

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Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders

Dec 19, 2016
Will Grathwohl, Aaron Wilson

* fixed typo in equation 16 

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