Grathwohl, Will

10 publications

NeurIPS 2023 DISCS: A Benchmark for Discrete Sampling Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai

NeurIPS 2022 Learning to Navigate Wikipedia by Taking Random Walks Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus

NeurIPS 2022 Score-Based Diffusion Meets Annealed Importance Sampling Arnaud Doucet, Will Grathwohl, Alexander G Matthews, Heiko Strathmann

ICML 2021 Oops I Took a Gradient: Scalable Sampling for Discrete Distributions Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison

ICML 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models Without Sampling Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel

ICLR 2020 Understanding the Limitations of Conditional Generative Models Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard Zemel

ICLR 2020 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, Kevin Swersky

ICLR 2019 FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

ICML 2019 Invertible Residual Networks Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen

ICLR 2018 Backpropagation Through the Void: Optimizing Control Variates for Black-Box Gradient Estimation Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud