Grathwohl, Will Sussman

8 publications

ICML 2024 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

ICMLW 2024 Variance Reduction of Diffusion Model's Gradients with Taylor Approximation-Based Control Variate Paul Jeha, Will Sussman Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen

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

ICLR 2023 Denoising Diffusion Samplers Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet

ICML 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

ICLRW 2022 Annealed Importance Sampling Meets Score Matching Arnaud Doucet, Will Sussman Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann

ICLRW 2021 No Conditional Models for Me: Training Joint EBMs on Mixed Continuous and Discrete Data Jacob Kelly, Will Sussman Grathwohl

ICLR 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