Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders. - Will Grathwohl; Aaron Wilson (CoRR, 2016)

Gradient-based Optimization of Neural Network Architecture. - Will Grathwohl; Elliot Creager; Seyed Kamyar Seyed Ghasemipour; Richard S. Zemel (ICLR (Workshop), 2018)

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. - Will Grathwohl; Dami Choi; Yuhuai Wu; Geoffrey Roeder; David Duvenaud (ICLR (Poster), 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)

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

Invertible Residual Networks. - Jens Behrmann; Will Grathwohl; Ricky T. Q. Chen; David Duvenaud; Jörn-Henrik Jacobsen (ICML, 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)

Understanding the Limitations of Conditional Generative Models. - Ethan Fetaya; Jörn-Henrik Jacobsen; Will Grathwohl; Richard S. 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 002; Kevin Swersky (ICLR, 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 (ICML, 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)

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 002; Kevin Swersky; David Duvenaud (ICLR, 2021)

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. - Will Grathwohl; Kevin Swersky; Milad Hashemi; David Duvenaud; Chris J. Maddison (ICML, 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)

Applications and Methods for Energy-based Models at Scale. - Will Grathwohl (2021)

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 (AISTATS, 2022)

Score-Based Diffusion meets Annealed Importance Sampling. - Arnaud Doucet; Will Grathwohl; Alexander G. de G. Matthews; Heiko Strathmann (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 001; Christine Kaeser-Chen; Rob Fergus (NeurIPS, 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)

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; et al. (CoRR, 2022)

Denoising Diffusion Samplers. - Francisco Vargas 001; Will Sussman Grathwohl; Arnaud Doucet (ICLR, 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 (ICML, 2023)

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

A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. - Nicholas Monath; Will Sussman Grathwohl; Michael Boratko; Rob Fergus; Andrew McCallum; Manzil Zaheer (ICML, 2024)

Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. - Paul Jeha; Will Grathwohl; Michael Riis Andersen; Carl Henrik Ek; Jes Frellsen (CoRR, 2024)