David Duvenaud

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Ryan P. AdamsPrinceton UniversityEmail confirmado em princeton.edu
Ricky Tian Qi ChenMeta FAIREmail confirmado em meta.com
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Email confirmado em utoronto.ca
Dougal MaclaurinPhD student, Harvard UniversityEmail confirmado em physics.harvard.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsEmail confirmado em recursionpharma.com
Will GrathwohlResearch Scientist, DeepmindEmail confirmado em deepmind.com
Jesse BettencourtUniversity of TorontoEmail confirmado em cs.toronto.edu
Rafael Gómez-BombarelliAssociate Professor, MIT DMSEEmail confirmado em mit.edu
Roger GrosseAssociate Professor, University of TorontoEmail confirmado em cs.toronto.edu
Yulia RubanovaGoogle DeepmindEmail confirmado em google.com
Jonathan LorraineResearch Scientist, NVIDIA; PhD, University of TorontoEmail confirmado em cs.toronto.edu
Jörn-Henrik JacobsenApple AI/ML ResearchEmail confirmado em vectorinstitute.ai
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleEmail confirmado em eng.cam.ac.uk
Xuechen LiStanford UniversityEmail confirmado em cs.stanford.edu
Jennifer N WeiOpen Molecular Software FoundationEmail confirmado em omsf.io
Kevin SwerskyGoogle BrainEmail confirmado em cs.toronto.edu
Joshua B. TenenbaumMITEmail confirmado em mit.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityEmail confirmado em cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeEmail confirmado em cam.ac.uk
Matthew J. JohnsonGoogle BrainEmail confirmado em csail.mit.edu

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David Duvenaud

Associate Professor, University of Toronto

Email confirmado em cs.toronto.edu - Página inicial

Deep Learning Differential Equations Approximate Inference


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	5790	2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	4489	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	3552	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	1512	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1111	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	1054*	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	1005	2016
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	960*	2019
Automatic model construction with Gaussian processes D Duvenaud	953	2014
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	701	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	653	2013
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	621	2019
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	561	2016
Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016	477	2016
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	440	2011
Optimizing Millions of Hyperparameters by Implicit Differentiation J Lorraine, P Vicol, D Duvenaud Artificial Intelligence and Statistics, 2019	436	2019
Scalable Gradients for Stochastic Differential Equations X Li, TKL Wong, RTQ Chen, D Duvenaud Artificial Intelligence and Statistics, 2020	422*	2020
Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019	411	2019
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	386*	2015
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	371	2019

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