David Duvenaud

Cited by

	All	Since 2015
Citations	8535	8399
h-index	34	34
i10-index	49	48

3400

1700

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2550

2013201420152016201720182019202024 69 132 298 635 1361 2556 3377

Co-authors

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 Chief Scientist, UberVerified email at eng.cam.ac.uk
Xuechen LiGoogleVerified email at cs.toronto.edu
Joshua B. TenenbaumMITVerified email at mit.edu
Jennifer N WeiGoogleVerified email at google.com
Jörn-Henrik JacobsenApple AI/ML ResearchVerified email at vectorinstitute.ai
Yulia RubanovaUniversity of TorontoVerified email at cs.toronto.edu
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
Sandeep Robert DattaAssociate Professor of Neurobiology, Harvard UniversityVerified email at hms.harvard.edu
Alexander Bame WiltschkoSenior Research Scientist, Google BrainVerified email at google.com
Jens BehrmannUniversity of BremenVerified email at uni-bremen.de

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	1583	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	973	2018
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	810	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International Conference on Machine Learning, 2113-2122, 2015	432	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	411	2016
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, RB Grosse, DK Duvenaud Advances in Neural Information Processing Systems, 2610-2620, 2018	359	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	353	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, 2946-2954, 2016	322	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	294	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	225	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	225	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	219	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	184	2014
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, RP Adams ICML workshop on Automatic Machine Learning, 2015	169*	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	156	2017
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	145	2018
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud arXiv preprint arXiv:1801.03558, 2018	111	2018
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	99*	2019
Sticking the landing: Simple, lower-variance gradient estimators for variational inference G Roeder, Y Wu, DK Duvenaud Advances in Neural Information Processing Systems, 6925-6934, 2017	98	2017
Avoiding Pathologies in Very Deep Networks D Duvenaud, O Rippel, R Adams, Z Ghahramani Artificial Intelligence and Statistics Conference, 2014	95	2014

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