David Duvenaud

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Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Ricky Tian Qi ChenFundamental AI Research, MetaVerified email at meta.com
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 GrathwohlResearch Scientist, DeepmindVerified email at deepmind.com
Rafael Gómez-BombarelliAssociate Professor, MIT DMSEVerified email at mit.edu
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Jesse BettencourtUniversity of TorontoVerified email at cs.toronto.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Jonathan LorraineResearch Scientist, NVIDIA; PhD, University of TorontoVerified email at cs.toronto.edu
Jörn-Henrik JacobsenApple AI/ML ResearchVerified email at vectorinstitute.ai
Xuechen LiStanford UniversityVerified email at cs.stanford.edu
Jennifer N WeiOpen Molecular Software FoundationVerified email at omsf.io
Yulia RubanovaDeepmindVerified email at google.com
Joshua B. TenenbaumMITVerified email at mit.edu
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerified email at cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Matthew J. JohnsonGoogle BrainVerified email at csail.mit.edu

David Duvenaud

Associate Professor, University of Toronto

Verified email at cs.toronto.edu - Homepage

Deep Learning Differential Equations Approximate Inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	4640	2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	4132	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	3072	2018
Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud Neural Information Processing Systems, arXiv preprint arXiv:1802.04942, 2018	1327	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1006	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	913*	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	905	2016
Automatic model construction with Gaussian processes D Duvenaud	826	2014
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	792*	2019
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	615	2013
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	590	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 29, 2016	534	2016
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	520	2019
Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016	437	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	398	2011
Optimizing Millions of Hyperparameters by Implicit Differentiation J Lorraine, P Vicol, D Duvenaud Artificial Intelligence and Statistics, 2019	360	2019
Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019	354	2019
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	340*	2015
Scalable Gradients for Stochastic Differential Equations X Li, TKL Wong, RTQ Chen, D Duvenaud Artificial Intelligence and Statistics, 2020	334*	2020
Efficient Graph Generation with Graph Recurrent Attention Networks R Liao, Y Li, Y Song, S Wang, C Nash, WL Hamilton, D Duvenaud, ... Neural Information Processing Systems, 2019	319	2019

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