Will Grathwohl
Will Grathwohl
Research Scientist, Deepmind
Verified email at - Homepage
Cited by
Cited by
Ffjord: Free-form continuous dynamics for scalable reversible generative models
W Grathwohl, RTQ Chen, J Bettencourt, I Sutskever, D Duvenaud
arXiv preprint arXiv:1810.01367, 2018
Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
International Conference on Machine Learning, 573-582, 2019
Your classifier is secretly an energy based model and you should treat it like one
W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, M Norouzi, ...
arXiv preprint arXiv:1912.03263, 2019
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud
arXiv preprint arXiv:1711.00123, 2017
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, R Zemel
International Conference on Machine Learning, 2020
Deep reinforcement learning and simulation as a path toward precision medicine
BK Petersen, J Yang, WS Grathwohl, C Cockrell, C Santiago, G An, ...
Journal of Computational Biology 26 (6), 597-604, 2019
Oops i took a gradient: Scalable sampling for discrete distributions
W Grathwohl, K Swersky, M Hashemi, D Duvenaud, C Maddison
International Conference on Machine Learning, 3831-3841, 2021
Understanding the limitations of conditional generative models
E Fetaya, JH Jacobsen, W Grathwohl, R Zemel
arXiv preprint arXiv:1906.01171, 2019
Disentangling space and time in video with hierarchical variational auto-encoders
W Grathwohl, A Wilson
arXiv preprint arXiv:1612.04440, 2016
Gradient-based optimization of neural network architecture
W Grathwohl, E Creager, SKS Ghasemipour, R Zemel
Joint energy-based models for semi-supervised classification
S Zhao, JH Jacobsen, W Grathwohl
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning 1, 2020
Optimal design of stochastic DNA synthesis protocols based on generative sequence models
EN Weinstein, AN Amin, WS Grathwohl, D Kassler, J Disset, D Marks
International Conference on Artificial Intelligence and Statistics, 7450-7482, 2022
Continuous diffusion for categorical data
S Dieleman, L Sartran, A Roshannai, N Savinov, Y Ganin, PH Richemond, ...
arXiv preprint arXiv:2211.15089, 2022
Self-conditioned embedding diffusion for text generation
R Strudel, C Tallec, F Altché, Y Du, Y Ganin, A Mensch, W Grathwohl, ...
arXiv preprint arXiv:2211.04236, 2022
No MCMC for me: Amortized samplers for fast and stable training of energy-based models
D Duvenaud, J Kelly, K Swersky, M Hashemi, M Norouzi, W Grathwohl
No conditional models for me: Training joint ebms on mixed continuous and discrete data
J Kelly, WS Grathwohl
Energy Based Models Workshop-ICLR 2021, 2021
Score-based diffusion meets annealed importance sampling
A Doucet, W Grathwohl, AG Matthews, H Strathmann
Advances in Neural Information Processing Systems 35, 21482-21494, 2022
Annealed Importance Sampling meets Score Matching
A Doucet, WS Grathwohl, AGG Matthews, H Strathmann
ICLR Workshop on Deep Generative Models for Highly Structured Data, 2022
Directly training joint energy-based models for conditional synthesis and calibrated prediction of multi-attribute data
J Kelly, R Zemel, W Grathwohl
arXiv preprint arXiv:2108.04227, 2021
Graph generation with energy-based models
J Liu, W Grathwohl, J Ba, K Swersky
ICML Workshop on Graph Representation Learning and Beyond (GRL+), 2020
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