David Duvenaud - Google Scholar Citations

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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
Ricky Tian Qi ChenPhD student, University 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 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
Jennifer N WeiGoogleVerified 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
Xuechen LiUniversity of TorontoVerified email at cs.toronto.edu
Yulia RubanovaUniversity of TorontoVerified email at cs.toronto.edu
Yuhuai(Tony) WuUniversity of TorontoVerified email at cs.toronto.edu
Dennis SheberlaPostdoctoral Associate, Harvard UniversityVerified email at g.harvard.edu
Benjamin Sanchez-LengelingResearch Scientist at Google BrainVerified email at google.com
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk

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	1057	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	607	2018
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	339	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International Conference on Machine Learning, 2113-2122, 2015	307	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	298	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	292	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, 2946-2954, 2016	229	2016
Automatic model construction with Gaussian processes D Duvenaud University of Cambridge, 2014	218	2014
Isolating sources of disentanglement in variational autoencoders TQ Chen, X Li, RB Grosse, DK Duvenaud Advances in Neural Information Processing Systems, 2610-2620, 2018	181	2018
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	178	2011
Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016	159	2016
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	158	2014
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, RP Adams ICML workshop on Automatic Machine Learning, 2015	118*	2015
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	107	2018
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	107	2017
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
Avoiding Pathologies in Very Deep Networks D Duvenaud, O Rippel, R Adams, Z Ghahramani Artificial Intelligence and Statistics Conference, 2014	70	2014
Probabilistic ODE Solvers with Runge-Kutta Means M Schober, D Duvenaud, P Hennig Neural Information Processing Systems, 2014	68	2014
Inference suboptimality in variational autoencoders C Cremer, X Li, D Duvenaud arXiv preprint arXiv:1801.03558, 2018	67	2018
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	61	2017

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