David Duvenaud - Google Scholar Citations

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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
Will GrathwohlUniversity of TorontoVerified email at cs.toronto.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
Jennifer N WeiGoogleVerified email at google.com
José Miguel Hernández-LobatoUniversity Lecturer (US Assistant Professor) University of CambridgeVerified email at cam.ac.uk
Yulia RubanovaUniversity of TorontoVerified email at cs.toronto.edu
Matthew J JohnsonGoogle BrainVerified email at csail.mit.edu
Geoffrey RoederPhD Student at Princeton UniversityVerified email at princeton.edu
Yuhuai(Tony) WuUniversity of TorontoVerified email at cs.toronto.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

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	914	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	507	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International Conference on Machine Learning, 2113-2122, 2015	285	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, 2016	275	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	272	2013
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	264	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	209	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	188	2014
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	168	2011
Isolating sources of disentanglement in variational autoencoders TQ Chen, X Li, RB Grosse, DK Duvenaud Advances in Neural Information Processing Systems, 2610-2620, 2018	152	2018
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	146	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	144	2016
Autograd: Reverse-mode differentiation of native Python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	103*	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	93	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	87	2018
Active Learning of Model Evidence Using Bayesian Quadrature MA Osborne, D Duvenaud, R Garnett, CE Rasmussen, SJ Roberts, ... Neural Information Processing Systems, 2012	72	2012
Probabilistic ODE Solvers with Runge-Kutta Means M Schober, D Duvenaud, P Hennig Neural Information Processing Systems, 2014	64	2014
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud arXiv preprint arXiv:1801.03558, 2018	63	2018
Avoiding Pathologies in Very Deep Networks D Duvenaud, O Rippel, R Adams, Z Ghahramani Artificial Intelligence and Statistics Conference, 2014	62	2014
Optimally-Weighted Herding is Bayesian Quadrature F Huszár, D Duvenaud Uncertainty in Artificial Intelligence, 2012, 2012	52	2012

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