Will Grathwohl

According to our database, Will Grathwohl authored at least 23 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

DISCS: A Benchmark for Discrete Sampling.

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, Dale Schuurmans, Hanjun Dai

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC.

Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl

Proceedings of the International Conference on Machine Learning, 2023

Denoising Diffusion Samplers.

Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Continuous diffusion for categorical data.

Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler

CoRR, 2022

Self-conditioned Embedding Diffusion for Text Generation.

Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, Rémi Leblond

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Score-Based Diffusion meets Annealed Importance Sampling.

Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal Design of Stochastic DNA Synthesis Protocols based on Generative Sequence Models.

Eli N. Weinstein, Alan Nawzad Amin, Will S. Grathwohl, Daniel Kassler, Jean Disset, Debora S. Marks

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Applications and Methods for Energy-based Models at Scale.

Will Grathwohl

PhD thesis, 2021

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

Jacob Kelly, Richard S. Zemel, Will Grathwohl

CoRR, 2021

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.

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

Proceedings of the 38th International Conference on Machine Learning, 2021

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, David Duvenaud

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

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

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel

CoRR, 2020

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling.

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the 8th International Conference on Learning Representations, 2020

Understanding the Limitations of Conditional Generative Models.

Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

J. Comput. Biol., 2019

Invertible Residual Networks.

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen

Proceedings of the 36th International Conference on Machine Learning, 2019

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.

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

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis.

Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol

CoRR, 2018

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

Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud

Proceedings of the 6th International Conference on Learning Representations, 2018

Gradient-based Optimization of Neural Network Architecture.

Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

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

Will Grathwohl, Aaron Wilson

CoRR, 2016