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David Duvenaud
Researcher at University of Toronto
Publications - 124
Citations - 19439
David Duvenaud is an academic researcher from University of Toronto. The author has contributed to research in topics: Artificial neural network & Estimator. The author has an hindex of 53, co-authored 119 publications receiving 15179 citations. Previous affiliations of David Duvenaud include University of Cambridge & University of British Columbia.
Papers
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Journal ArticleDOI
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gómez-Bombarelli,Jennifer N. Wei,David Duvenaud,José Miguel Hernández-Lobato,Benjamin Sanchez-Lengeling,Dennis Sheberla,Jorge Aguilera-Iparraguirre,Timothy D. Hirzel,Ryan P. Adams,Alán Aspuru-Guzik,Alán Aspuru-Guzik +10 more
TL;DR: In this article, a deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor, which can generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds.
Proceedings Article
Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud,Dougal Maclaurin,Jorge Aguilera-Iparraguirre,Rafael Gómez-Bombarelli,Timothy D. Hirzel,Alán Aspuru-Guzik,Ryan P. Adams +6 more
TL;DR: In this paper, a convolutional neural network that operates directly on graphs is proposed to learn end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.
Journal ArticleDOI
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli,Jennifer N. Wei,David Duvenaud,José Miguel Hernández-Lobato,Benjamin Sanchez-Lengeling,Dennis Sheberla,Jorge Aguilera-Iparraguirre,Timothy D. Hirzel,Ryan P. Adams,Alán Aspuru-Guzik,Alán Aspuru-Guzik +10 more
TL;DR: A method to convert discrete representations of molecules to and from a multidimensional continuous representation that allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds is reported.
Proceedings Article
Neural ordinary differential equations
TL;DR: In this paper, the authors introduce a new family of deep neural network models called continuous normalizing flows, which parameterize the derivative of the hidden state using a neural network, and the output of the network is computed using a black-box differential equation solver.
Posted Content
Neural Ordinary Differential Equations
TL;DR: In this paper, the authors introduce a new family of deep neural network models called continuous normalizing flows, which parameterize the derivative of the hidden state using a neural network, and the output of the network is computed using a black-box differential equation solver.