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Complex Momentum for Learning in Games


Feb 16, 2021
Jonathan Lorraine, David Acuna, Paul Vicol, David Duvenaud


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Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations


Feb 12, 2021
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud


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Oops I Took A Gradient: Scalable Sampling for Discrete Distributions


Feb 08, 2021
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

* Energy-Based Models, Deep generative models, MCMC sampling 

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Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering


Nov 09, 2020
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig


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Teaching with Commentaries


Nov 05, 2020
Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton


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No MCMC for me: Amortized sampling for fast and stable training of energy-based models


Oct 14, 2020
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud


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A Study of Gradient Variance in Deep Learning


Jul 09, 2020
Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba


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Learning Differential Equations that are Easy to Solve


Jul 09, 2020
Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud


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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models


Apr 01, 2020
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

* ICLR 2020 

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What went wrong and when? Instance-wise Feature Importance for Time-series Models


Mar 05, 2020
Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg


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Scalable Gradients for Stochastic Differential Equations


Feb 24, 2020
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

* AISTATS 2020; 25 pages, 6 figures in main text; fixed various typos 

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


Feb 14, 2020
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel


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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One


Dec 11, 2019
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky


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Neural Networks with Cheap Differential Operators


Dec 08, 2019
Ricky T. Q. Chen, David Duvenaud

* NeurIPS 2019 

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Optimizing Millions of Hyperparameters by Implicit Differentiation


Nov 06, 2019
Jonathan Lorraine, Paul Vicol, David Duvenaud

* Submitted to AISTATS 2020 

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Efficient Graph Generation with Graph Recurrent Attention Networks


Oct 02, 2019
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel

* Neural Information Processing Systems (NeurIPS) 2019 

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Understanding Undesirable Word Embedding Associations


Aug 18, 2019
Kawin Ethayarajh, David Duvenaud, Graeme Hirst

* Accepted to ACL 2019 

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Latent ODEs for Irregularly-Sampled Time Series


Jul 08, 2019
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud


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Residual Flows for Invertible Generative Modeling


Jun 07, 2019
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen

* fix typo in abstract 

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Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions


Mar 07, 2019
Matthew MacKay, Paul Vicol, Jon Lorraine, David Duvenaud, Roger Grosse

* Published as a conference paper at ICLR 2019 

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Invertible Residual Networks


Nov 02, 2018
Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen


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Towards Understanding Linear Word Analogies


Oct 27, 2018
Kawin Ethayarajh, David Duvenaud, Graeme Hirst


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Isolating Sources of Disentanglement in Variational Autoencoders


Oct 22, 2018
Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud

* Added more experiments and improved clarity 

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Neural Ordinary Differential Equations


Oct 22, 2018
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud


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FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models


Oct 22, 2018
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

* 8 Pages, 6 figures 

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