David Duvenaud

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Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Ricky Tian Qi ChenPhD student, University of TorontoVerified email at cs.toronto.edu
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
Roger GrosseAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Will GrathwohlUniversity of TorontoVerified email at cs.toronto.edu
Rafael Gómez-BombarelliAssistant Professor, MIT DMSEVerified email at mit.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
Joshua B. TenenbaumMITVerified email at mit.edu
Jennifer N WeiGoogleVerified email at google.com
Yulia RubanovaDeepmindVerified email at google.com
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Geoffrey RoederPhD Student at Princeton UniversityVerified email at princeton.edu
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of TorontoVerified email at utoronto.ca
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Sandeep Robert DattaAssociate Professor of Neurobiology, Harvard UniversityVerified email at hms.harvard.edu

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	1838	2015
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	1146	2018
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	1091	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	483	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	469	2016
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud arXiv preprint arXiv:1802.04942, 2018	442	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	384	2013
Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, D Duvenaud, AB Wiltschko, SR Datta, RP Adams arXiv preprint arXiv:1603.06277, 2016	350	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	335	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	286	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	253	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	241	2011
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	196	2014
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	192	2018
Autograd: Reverse-mode differentiation of native Python D Maclaurin, D Duvenaud, RP Adams ICML workshop on Automatic Machine Learning, 2015	192*	2015
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	169	2017
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	150*	2019
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud International Conference on Machine Learning, 1078-1086, 2018	136	2018
Avoiding Pathologies in Very Deep Networks D Duvenaud, O Rippel, R Adams, Z Ghahramani Artificial Intelligence and Statistics Conference, 2014	112	2014
Noisy natural gradient as variational inference G Zhang, S Sun, D Duvenaud, R Grosse International Conference on Machine Learning, 5852-5861, 2018	108	2018

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