David Duvenaud
David Duvenaud
Assistant Professor, University of Toronto
Verified email at cs.toronto.edu - Homepage
Cited by
Cited by
Convolutional Networks on Graphs for Learning Molecular Fingerprints
D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ...
Neural Information Processing Systems, 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
Neural Ordinary Differential Equations
RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud
Neural Information Processing Systems, 2018
Gradient-based hyperparameter optimization through reversible learning
D Maclaurin, D Duvenaud, R Adams
International Conference on Machine Learning, 2113-2122, 2015
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
Isolating sources of disentanglement in variational autoencoders
RTQ Chen, X Li, RB Grosse, DK Duvenaud
Advances in neural information processing systems, 2610-2620, 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
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, 2946-2954, 2016
Automatic model construction with Gaussian processes
D Duvenaud
University of Cambridge, 2014
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
Neural networks for the prediction of organic chemistry reactions
JN Wei, D Duvenaud, A Aspuru-Guzik
ACS central science 2 (10), 725-732, 2016
Additive Gaussian Processes
D Duvenaud, H Nickisch, CE Rasmussen
Neural Information Processing Systems, 2011
Automatic construction and natural-language description of nonparametric regression models
JR Lloyd, D Duvenaud, R Grosse, JB Tenenbaum, Z Ghahramani
Association for the Advancement of Artificial Intelligence, 2014
Autograd: Reverse-mode differentiation of native python
D Maclaurin, D Duvenaud, RP Adams
ICML workshop on Automatic Machine Learning, 2015
Invertible residual networks
J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen
International Conference on Machine Learning, 2018
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud
arXiv preprint arXiv:1711.00123, 2017
Inference suboptimality in variational autoencoders
C Cremer, X Li, D Duvenaud
arXiv preprint arXiv:1801.03558, 2018
Latent ODEs for irregularly-sampled time series
Y Rubanova, RTQ Chen, D Duvenaud
Neural Information Processing Systems, 2019
Avoiding Pathologies in Very Deep Networks
D Duvenaud, O Rippel, R Adams, Z Ghahramani
Artificial Intelligence and Statistics Conference, 2014
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
G Roeder, Y Wu, DK Duvenaud
Advances in Neural Information Processing Systems, 6925-6934, 2017
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