David Duvenaud - Google Scholar Citations

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	All	Since 2014
Citations	4136	4093
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Co-authors

Ryan P. AdamsPrinceton UniversityVerified email at princeton.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
Rafael Gómez-BombarelliAssistant Professor, MIT DMSEVerified email at mit.edu
Ricky Tian Qi ChenPhD student, University of TorontoVerified email at cs.toronto.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberVerified email at eng.cam.ac.uk
Joshua B. TenenbaumMITVerified email at mit.edu
Jennifer N WeiGoogleVerified email at google.com
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Will GrathwohlUniversity of TorontoVerified email at cs.toronto.edu
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Matthew J JohnsonGoogle BrainVerified email at csail.mit.edu
Yulia RubanovaUniversity of TorontoVerified email at cs.toronto.edu
Geoffrey RoederPhD Student at Princeton UniversityVerified email at princeton.edu
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Sandeep Robert DattaAssociate Professor of Neurobiology, Harvard UniversityVerified email at hms.harvard.edu
Alexander Bame WiltschkoSenior Research Scientist, Google BrainVerified email at google.com
Yuhuai(Tony) WuUniversity of TorontoVerified email at cs.toronto.edu

David Duvenaud

Assistant Professor, University of Toronto

Verified email at cs.toronto.edu - Homepage

Machine Learning Bayesian Statistics Approximate Inference


Title	Cited by	Year
Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	803	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	445	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International Conference on Machine Learning, 2113-2122, 2015	264	2015
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	262	2013
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, 2016	257	2016
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	196	2018
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, 2946-2954, 2016	192	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	180	2014
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	162	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	139	2014
Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016	131	2016
Isolating sources of disentanglement in variational autoencoders TQ Chen, X Li, RB Grosse, DK Duvenaud Advances in Neural Information Processing Systems, 2610-2620, 2018	128	2018
Autograd: Reverse-mode differentiation of native Python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	93*	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	80	2017
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	74	2018
Active Learning of Model Evidence Using Bayesian Quadrature MA Osborne, D Duvenaud, R Garnett, CE Rasmussen, SJ Roberts, ... Neural Information Processing Systems, 2012	68	2012
Probabilistic ODE Solvers with Runge-Kutta Means M Schober, D Duvenaud, P Hennig Neural Information Processing Systems, 2014	63	2014
Avoiding Pathologies in Very Deep Networks D Duvenaud, O Rippel, R Adams, Z Ghahramani Artificial Intelligence and Statistics Conference, 2014	57	2014
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud arXiv preprint arXiv:1801.03558, 2018	55	2018
Optimally-Weighted Herding is Bayesian Quadrature F Huszár, D Duvenaud Uncertainty in Artificial Intelligence, 2012, 2012	49	2012

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