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David Duvenaud
David Duvenaud
Associate Professor, University of Toronto
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Neural Ordinary Differential Equations
RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud
Neural Information Processing Systems, 2018
40552018
Convolutional Networks on Graphs for Learning Molecular Fingerprints
D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ...
Neural Information Processing Systems, 2015
38782015
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
27932018
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
12132018
Gradient-based hyperparameter optimization through reversible learning
D Maclaurin, D Duvenaud, R Adams
International conference on machine learning, 2113-2122, 2015
9512015
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
8472016
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
836*2018
Automatic model construction with Gaussian processes
D Duvenaud
University of Cambridge, 2014
7662014
Latent ODEs for irregularly-sampled time series
Y Rubanova, RTQ Chen, D Duvenaud
Neural Information Processing Systems, 2019
705*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
6012013
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
5612016
Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
International Conference on Machine Learning, 2018
5452018
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
4492019
Neural networks for the prediction of organic chemistry reactions
JN Wei, D Duvenaud, A Aspuru-Guzik
ACS central science 2 (10), 725-732, 2016
4102016
Additive Gaussian Processes
D Duvenaud, H Nickisch, CE Rasmussen
Neural Information Processing Systems, 2011
3852011
Residual Flows for Invertible Generative Modeling
RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen
Neural Information Processing Systems, 2019
3312019
Autograd: Reverse-mode differentiation of native Python
D Maclaurin, D Duvenaud, M Johnson, RP Adams
ICML workshop on Automatic Machine Learning, 2015
323*2015
Optimizing Millions of Hyperparameters by Implicit Differentiation
J Lorraine, P Vicol, D Duvenaud
Artificial Intelligence and Statistics, 2019
3152019
Scalable Gradients for Stochastic Differential Equations
X Li, TKL Wong, RTQ Chen, D Duvenaud
Artificial Intelligence and Statistics, 2020
295*2020
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud
International Conference on Learning Representations, arXiv preprint arXiv …, 2017
2952017
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