David Duvenaud

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Ryan P. AdamsPrinceton UniversityVerifierad e-postadress på princeton.edu
Ricky Tian Qi ChenMeta FAIRVerifierad e-postadress på meta.com
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verifierad e-postadress på utoronto.ca
Dougal MaclaurinPhD student, Harvard UniversityVerifierad e-postadress på physics.harvard.edu
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsVerifierad e-postadress på recursionpharma.com
Will GrathwohlResearch Scientist, DeepmindVerifierad e-postadress på deepmind.com
Jesse BettencourtUniversity of TorontoVerifierad e-postadress på cs.toronto.edu
Rafael Gómez-BombarelliAssociate Professor, MIT DMSEVerifierad e-postadress på mit.edu
Roger GrosseAssociate Professor, University of TorontoVerifierad e-postadress på cs.toronto.edu
Yulia RubanovaGoogle DeepmindVerifierad e-postadress på google.com
Jonathan LorraineResearch Scientist, NVIDIA; PhD, University of TorontoVerifierad e-postadress på cs.toronto.edu
Jörn-Henrik JacobsenApple AI/ML ResearchVerifierad e-postadress på vectorinstitute.ai
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerifierad e-postadress på eng.cam.ac.uk
Xuechen LiStanford UniversityVerifierad e-postadress på cs.stanford.edu
Jennifer N WeiOpen Molecular Software FoundationVerifierad e-postadress på omsf.io
Kevin SwerskyGoogle BrainVerifierad e-postadress på cs.toronto.edu
Joshua B. TenenbaumMITVerifierad e-postadress på mit.edu
James Robert LloydPhD from Machine Learning Group, Cambridge UniversityVerifierad e-postadress på cam.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerifierad e-postadress på cam.ac.uk
Matthew J. JohnsonGoogle BrainVerifierad e-postadress på csail.mit.edu

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David Duvenaud

Associate Professor, University of Toronto

Verifierad e-postadress på cs.toronto.edu - Startsida

Deep Learning Differential Equations Approximate Inference


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018	6176	2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015	4607	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	3711	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	1552	2018
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1134	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	1095*	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	1035	2016
Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019	1004*	2019
Automatic model construction with Gaussian processes D Duvenaud	985	2014
Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018	726	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	665	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	644	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	569	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	485	2016
Optimizing Millions of Hyperparameters by Implicit Differentiation J Lorraine, P Vicol, D Duvenaud Artificial Intelligence and Statistics, 2019	463	2019
Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011	451	2011
Scalable Gradients for Stochastic Differential Equations X Li, TKL Wong, RTQ Chen, D Duvenaud Artificial Intelligence and Statistics, 2020	441*	2020
Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019	423	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	393	2019
Autograd: Reverse-mode differentiation of native python D Maclaurin, D Duvenaud, M Johnson, RP Adams ICML workshop on Automatic Machine Learning, 2015	391*	2015

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