David Duvenaud

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Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Ricky Tian Qi ChenUniversity 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
Will GrathwohlUniversity of TorontoVerified email at cs.toronto.edu
Roger GrosseAssistant Professor, University 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
Jennifer N WeiGoogleVerified email at google.com
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
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
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
Geoffrey RoederPrinceton UniversityVerified email at princeton.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	2074	2015
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	1346	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	1307	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	535	2015
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud arXiv preprint arXiv:1802.04942, 2018	516	2018
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	513	2016
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	414	2013
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	383	2014
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, 2946-2954, 2016	382	2016
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	339	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	276	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	258	2011
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	237	2018
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	211	2019
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	208	2014
Autograd: Reverse-mode differentiation of native Python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	203*	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	181	2017
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud International Conference on Machine Learning, 1078-1086, 2018	157	2018
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	137	2019
Noisy natural gradient as variational inference G Zhang, S Sun, D Duvenaud, R Grosse International Conference on Machine Learning, 5852-5861, 2018	131	2018

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