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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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Explaining Image Classifiers by Counterfactual Generation

Oct 11, 2018
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud


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Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples

Sep 08, 2018
George A. Adam, Petr Smirnov, David Duvenaud, Benjamin Haibe-Kains, Anna Goldenberg


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Scalable Recommender Systems through Recursive Evidence Chains

Jul 05, 2018
Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud


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Inference Suboptimality in Variational Autoencoders

May 27, 2018
Chris Cremer, Xuechen Li, David Duvenaud

* ICML 

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Stochastic Hyperparameter Optimization through Hypernetworks

Mar 08, 2018
Jonathan Lorraine, David Duvenaud

* 9 pages, 6 figures; revised figures 

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