David Duvenaud

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Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Ricky Tian Qi ChenResearch Scientist, FAIRVerified email at fb.com
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
Dougal MaclaurinPhD student, Harvard UniversityVerified email at physics.harvard.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerified email at recursionpharma.com
Will GrathwohlResearch Scientist, DeepmindVerified email at deepmind.com
Rafael Gómez-BombarelliAssistant Professor, MIT DMSEVerified email at mit.edu
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Jesse BettencourtUniversity of TorontoVerified email at cs.toronto.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Xuechen LiPhD Student, Stanford UniversityVerified email at stanford.edu
Jörn-Henrik JacobsenApple AI/ML ResearchVerified email at vectorinstitute.ai
Jennifer N WeiGoogleVerified email at google.com
Jonathan LorraineResearch Scientist Intern, NVIDIA; PhD Candidate, University of Toronto; Vector InstituteVerified email at cs.toronto.edu
Joshua B. TenenbaumMITVerified email at mit.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Yulia RubanovaDeepmindVerified email at google.com
Matthew J. JohnsonGoogle BrainVerified email at csail.mit.edu
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
José Miguel Hernández-LobatoAssociate Professor in Machine Learning, University of CambridgeVerified email at cam.ac.uk

David Duvenaud

Assistant Professor, University of Toronto

Verified email at cs.toronto.edu - Homepage

Machine Learning Bayesian Statistics Approximate Inference


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Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	2916	2015
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	2368	2018
Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018	1906	2018
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud Neural Information Processing Systems, arXiv preprint arXiv:1802.04942, 2018	817	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	707	2015
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach R Gómez-Bombarelli, J Aguilera-Iparraguirre, TD Hirzel, D Duvenaud, ... Nature materials 15 (10), 1120-1127, 2016	666	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	533	2014
FFJORD: Free-form continuous dynamics for scalable reversible generative models W Grathwohl, RTQ Chen, J Betterncourt, I Sutskever, D Duvenaud International Conference on Learning Representations, 2018	528*	2018
Structure Discovery in Nonparametric Regression through Compositional Kernel Search D Duvenaud, JR Lloyd, R Grosse, JB Tenenbaum, Z Ghahramani International Conference on Machine Learning, 2013	488	2013
Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, DK Duvenaud, A Wiltschko, RP Adams, SR Datta Advances in neural information processing systems 29, 2016	483	2016
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	380*	2019
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	364	2018
Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016	332	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	312	2011
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, ... International Conference on Learning Representations 2020, 2019	247	2019
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	247*	2015
Automatic construction and natural-language description of nonparametric regression models JR Lloyd, D Duvenaud, R Grosse, JB Tenenbaum, Z Ghahramani Association for the Advancement of Artificial Intelligence, 2014	238	2014
Backpropagation through the void: Optimizing control variates for black-box gradient estimation W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud International Conference on Learning Representations, arXiv preprint arXiv …, 2017	235	2017
Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019	205	2019
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud International Conference on Machine Learning, 1078-1086, 2018	205	2018

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