Will Grathwohl
Will Grathwohl
Research Scientist, Deepmind
Подтвержден адрес электронной почты в домене deepmind.com - Главная страница
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
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
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
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
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
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
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
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
Graph generation with energy-based models
J Liu, W Grathwohl, J Ba, K Swersky
ICML Workshop on Graph Representation Learning and Beyond (GRL+), 2020
Training Glow with constant memory cost
X Li, W Grathwohl
NIPS Workshop on Bayesian Deep Learning, 2018
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
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
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
Few-shot learning for free by modelling global class structure
X Li, W Grathwohl, E Triantafillou, D Duvenaud, R Zemel
2nd Workshop on Meta-Learning at NeurIPS, 2018
Using digital ultrasound to investigate trill vibration.
DH Whalen, K Iskarous, W Grathwohl, M Proctor
The Journal of the Acoustical Society of America 128 (4), 2289-2289, 2010
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
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