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Invited Talk David Duvenaud
David Duvenaud
Sat Dec 12 01:17 PM -- 01:45 PM (PST) @
Author Information
David Duvenaud (University of Toronto)
David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.
More from the Same Authors
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2022 Workshop: The Symbiosis of Deep Learning and Differential Equations II »
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Qiyao Wei · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud -
2021 : Dependent Types for Machine Learning in Dex - David Duvenaud - University of Toronto »
David Duvenaud · AIPLANS 2021 -
2021 Poster: Meta-learning to Improve Pre-training »
Aniruddh Raghu · Jonathan Lorraine · Simon Kornblith · Matthew McDermott · David Duvenaud -
2020 : Panel discussion 2 »
Danielle S Bassett · Yoshua Bengio · Cristina Savin · David Duvenaud · Anna Choromanska · Yanping Huang -
2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization Q&A »
David Duvenaud · J. Zico Kolter · Matthew Johnson -
2020 Poster: What went wrong and when? Instance-wise feature importance for time-series black-box models »
Sana Tonekaboni · Shalmali Joshi · Kieran Campbell · David Duvenaud · Anna Goldenberg -
2020 Poster: Learning Differential Equations that are Easy to Solve »
Jacob Kelly · Jesse Bettencourt · Matthew Johnson · David Duvenaud -
2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization »
David Duvenaud · J. Zico Kolter · Matthew Johnson -
2019 Workshop: Program Transformations for ML »
Pascal Lamblin · Atilim Gunes Baydin · Alexander Wiltschko · Bart van Merriënboer · Emily Fertig · Barak Pearlmutter · David Duvenaud · Laurent Hascoet -
2019 : Molecules and Genomes »
David Haussler · Djork-Arné Clevert · Michael Keiser · Alan Aspuru-Guzik · David Duvenaud · David Jones · Jennifer Wei · Alexander D'Amour -
2019 Poster: Latent Ordinary Differential Equations for Irregularly-Sampled Time Series »
Yulia Rubanova · Tian Qi Chen · David Duvenaud -
2019 Poster: Residual Flows for Invertible Generative Modeling »
Tian Qi Chen · Jens Behrmann · David Duvenaud · Joern-Henrik Jacobsen -
2019 Spotlight: Residual Flows for Invertible Generative Modeling »
Tian Qi Chen · Jens Behrmann · David Duvenaud · Joern-Henrik Jacobsen -
2019 Poster: Efficient Graph Generation with Graph Recurrent Attention Networks »
Renjie Liao · Yujia Li · Yang Song · Shenlong Wang · Will Hamilton · David Duvenaud · Raquel Urtasun · Richard Zemel -
2019 Poster: Neural Networks with Cheap Differential Operators »
Tian Qi Chen · David Duvenaud -
2019 Spotlight: Neural Networks with Cheap Differential Operators »
Tian Qi Chen · David Duvenaud -
2018 : Software Panel »
Ben Letham · David Duvenaud · Dustin Tran · Aki Vehtari -
2018 Poster: Isolating Sources of Disentanglement in Variational Autoencoders »
Tian Qi Chen · Xuechen (Chen) Li · Roger Grosse · David Duvenaud -
2018 Oral: Isolating Sources of Disentanglement in Variational Autoencoders »
Tian Qi Chen · Xuechen (Chen) Li · Roger Grosse · David Duvenaud -
2018 Poster: Neural Ordinary Differential Equations »
Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud -
2018 Oral: Neural Ordinary Differential Equations »
Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud -
2017 Workshop: Aligned Artificial Intelligence »
Dylan Hadfield-Menell · Jacob Steinhardt · David Duvenaud · David Krueger · Anca Dragan -
2017 : Automatic Chemical Design Using a Data-driven Continuous Representation of Molecules »
David Duvenaud -
2017 Poster: Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference »
Geoffrey Roeder · Yuhuai Wu · David Duvenaud -
2016 : Generating Class-conditional Images with Gradient-based Inference »
David Duvenaud -
2016 : David Duvenaud – No more mini-languages: The power of autodiffing full-featured Python »
David Duvenaud -
2016 Workshop: Reliable Machine Learning in the Wild »
Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy Liang -
2016 Poster: Composing graphical models with neural networks for structured representations and fast inference »
Matthew Johnson · David Duvenaud · Alex Wiltschko · Ryan Adams · Sandeep R Datta -
2016 Poster: Probing the Compositionality of Intuitive Functions »
Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman -
2015 : *David Duvenaud* Automatic Differentiation: The most criminally underused tool in probabilistic numerics »
David Duvenaud -
2015 Poster: Convolutional Networks on Graphs for Learning Molecular Fingerprints »
David Duvenaud · Dougal Maclaurin · Jorge Iparraguirre · Rafael Bombarell · Timothy Hirzel · Alan Aspuru-Guzik · Ryan Adams -
2014 Poster: Probabilistic ODE Solvers with Runge-Kutta Means »
Michael Schober · David Duvenaud · Philipp Hennig -
2014 Oral: Probabilistic ODE Solvers with Runge-Kutta Means »
Michael Schober · David Duvenaud · Philipp Hennig -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2011 Poster: Additive Gaussian Processes »
David Duvenaud · Hannes Nickisch · Carl Edward Rasmussen