David Duvenaud

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	Toate	Din 2020
Referințe bibliografice	34784	29212
h-index	54	54
i10-index	80	76

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Ryan P. AdamsPrinceton UniversityAdresă de e-mail confirmată pe princeton.edu
Ricky Tian Qi ChenMeta FAIRAdresă de e-mail confirmată pe meta.com
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Adresă de e-mail confirmată pe utoronto.ca
Dougal MaclaurinPhD student, Harvard UniversityAdresă de e-mail confirmată pe physics.harvard.edu
Jesse BettencourtUniversity of TorontoAdresă de e-mail confirmată pe cs.toronto.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsAdresă de e-mail confirmată pe recursionpharma.com
Will GrathwohlResearch Scientist, DeepmindAdresă de e-mail confirmată pe deepmind.com
Rafael Gómez-BombarelliAssociate Professor, MIT DMSEAdresă de e-mail confirmată pe mit.edu
Roger GrosseAssociate Professor, University of TorontoAdresă de e-mail confirmată pe cs.toronto.edu
Yulia RubanovaGoogle DeepmindAdresă de e-mail confirmată pe google.com
Jörn-Henrik JacobsenApple AI/ML ResearchAdresă de e-mail confirmată pe vectorinstitute.ai
Xuechen LiStanford UniversityAdresă de e-mail confirmată pe cs.stanford.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleAdresă de e-mail confirmată pe eng.cam.ac.uk
Jonathan LorraineResearch Scientist, NVIDIA; PhD, University of TorontoAdresă de e-mail confirmată pe cs.toronto.edu
Jennifer N WeiOpen Molecular Software FoundationAdresă de e-mail confirmată pe omsf.io
Kevin SwerskyGoogle BrainAdresă de e-mail confirmată pe cs.toronto.edu
Joshua B. TenenbaumMITAdresă de e-mail confirmată pe mit.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityAdresă de e-mail confirmată pe cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeAdresă de e-mail confirmată pe cam.ac.uk
Matthew J. JohnsonGoogle BrainAdresă de e-mail confirmată pe csail.mit.edu

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David Duvenaud

Associate Professor, University of Toronto

Adresă de e-mail confirmată pe cs.toronto.edu - Pagina de pornire

Deep Learning Differential Equations Approximate Inference


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	6301	2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	4589	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	3740	2018
Advances in neural information processing systems X Chen, Y Rubanova, J Bettencourt, DK Duvenaud Neural Ordinary Differential Equations 31, 2018	1698	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	1554	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1140	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	1108*	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	1046	2016
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	1027*	2019
Automatic model construction with Gaussian processes D Duvenaud	988	2014
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	731	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	663	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	654	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	571	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	494	2016
Optimizing Millions of Hyperparameters by Implicit Differentiation J Lorraine, P Vicol, D Duvenaud Artificial Intelligence and Statistics, 2019	473	2019
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	458	2011
Scalable Gradients for Stochastic Differential Equations X Li, TKL Wong, RTQ Chen, D Duvenaud Artificial Intelligence and Statistics, 2020	452*	2020
Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019	426	2019
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	392	2019

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