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David Duvenaud

David Duvenaud

D-Index & Metrics

Computer Science

D-Index
52
Citations
21164
World Ranking
4951
National Ranking
195

Overview

David Duvenaud is affiliated with the University of Toronto in Canada and has a research focus primarily in the field of Computer Science, with numerous contributions to subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Hardware and Architecture, and Management Science and Operations Research.

Their research spans several main topics including Adversarial Robustness in Machine Learning, Gaussian Processes and Bayesian Inference, Generative Adversarial Networks and Image Synthesis, Model Reduction and Neural Networks, Stochastic Gradient Optimization Techniques, Topic Modeling, and Explainable Artificial Intelligence (XAI).

David Duvenaud has published extensively in various venues, with the majority of their work appearing in arXiv (Cornell University). Other publication venues include the Proceedings of the ACM on Programming Languages.

Recent papers authored or co-authored by David Duvenaud include:

  • "Scalable Gradients for Stochastic Differential Equations" (2020, arXiv (Cornell University))
  • "Towards Understanding Sycophancy in Language Models" (2023, arXiv (Cornell University))
  • "Learning Differential Equations that are Easy to Solve" (2020, arXiv (Cornell University))
  • "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" (2024, arXiv (Cornell University))
  • "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling" (2020, arXiv (Cornell University))

Frequent co-authors in David Duvenaud's collaborations include:

  • Ricky T. Q. Chen
  • Samuel R. Bowman
  • Ethan Perez
  • Dami Choi
  • Shauna Kravec

Best Publications

  • Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

    Rafael Gómez-Bombarelli;Jennifer Nansean Wei;David Duvenaud;José Miguel Hernández-Lobato

  • Convolutional networks on graphs for learning molecular fingerprints

    David Duvenaud;Dougal Maclaurin;Jorge Aguilera-Iparraguirre;Rafael Gómez-Bombarelli

  • Neural ordinary differential equations

    Ricky T. Q. Chen;Yulia Rubanova;Jesse Bettencourt;David Duvenaud

  • Isolating Sources of Disentanglement in Variational Autoencoders.

    Tian Qi Chen;Xuechen Li;Roger B. Grosse;David Duvenaud

  • Automatic model construction with Gaussian processes

    David Duvenaud

  • Gradient-based Hyperparameter Optimization through Reversible Learning

    Dougal Maclaurin;David Duvenaud;Ryan Adams

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Lloyd;Roger Grosse;Joshua Tenenbaum

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

    Will Grathwohl;Ricky T. Q. Chen;Jesse Bettencourt;Ilya Sutskever

  • Isolating Sources of Disentanglement in Variational Autoencoders

    Ricky T. Q. Chen;Xuechen Li;Roger Grosse;David Duvenaud

  • Neural networks for the prediction organic chemistry reactions

    Jennifer N. Wei;David Duvenaud;Alán Aspuru-Guzik

  • Neural Networks for the Prediction of Organic Chemistry Reactions

    Jennifer N. Wei;David Duvenaud;Alán Aspuru-Guzik

  • Composing graphical models with neural networks for structured representations and fast inference

    Matthew J. Johnson;David Duvenaud;Alexander B. Wiltschko;Ryan P. Adams

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Robert Lloyd;Roger Grosse;Joshua B. Tenenbaum

  • Invertible Residual Networks

    Jens Behrmann;Will Grathwohl;Ricky T. Q. Chen;David Duvenaud

  • Additive Gaussian Processes

    David K Duvenaud;Hannes Nickisch;Carl E. Rasmussen

  • Composing graphical models with neural networks for structured representations and fast inference

    Matthew J. Johnson;David Duvenaud;Alexander B. Wiltschko;Sandeep R. Datta

  • Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

    Yulia Rubanova;Ricky T. Q. Chen;David K. Duvenaud

  • Automatic construction and natural-language description of nonparametric regression models

    James Robert Lloyd;David Duvenaud;Roger Grosse;Joshua B. Tenenbaum

  • Latent ODEs for Irregularly-Sampled Time Series

    Yulia Rubanova;Ricky T. Q. Chen;David Duvenaud

  • Your classifier is secretly an energy based model and you should treat it like one

    Will Grathwohl;Kuan-Chieh Wang;Joern-Henrik Jacobsen;David Duvenaud

  • Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

    Will Grathwohl;Dami Choi;Yuhuai Wu;Geoffrey Roeder

  • Scalable Gradients for Stochastic Differential Equations

    Xuechen Li;Ting-Kam Leonard Wong;Ricky T. Q. Chen;David Duvenaud

  • Efficient Graph Generation with Graph Recurrent Attention Networks

    Renjie Liao;Yujia Li;Yang Song;Shenlong Wang

Frequent Co-Authors

Ryan P. Adams
Ryan P. Adams Princeton University
Roger Grosse
Roger Grosse University of Toronto
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Richard S. Zemel
Richard S. Zemel University of Toronto
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Kevin Swersky
Kevin Swersky Google (United States)
Graeme Hirst
Graeme Hirst University of Toronto
Samuel J. Gershman
Samuel J. Gershman Harvard University

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