David Duvenaud is an Associate Professor in Computer Science and Statistics at the University of Toronto. He holds a Sloan Research Fellowship, a Canada Research Chair in Generative Models, and a Canada CIFAR AI chair. His research focuses on deep learning and AI governance. His postdoc was done at Harvard University and his Ph.D. at the University of Cambridge. He is a Founding Member of the Vector Institute for Artificial Intelligence.
Faculty Member, Vector Institute
Associate Professor, Department of Statistical Sciences and Department of Computer Science, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Deep Learning
- Generative Models
- AI Governance
- AI Alignment
Highlights
- Holds a Tier 2 Canada Research Chair in Generative Models
- Co-founder of Fable Therapeutics
- Alfred P. Sloan Research Fellow in Computer Science
Publications
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming
2021
Meta-Learning for Semi-Supervised Few-Shot Classification
International Conference on Learning Representations (ICLR) 2018
Stochastic Hyperparameter Optimization through Hypernetworks
2018
Isolating Sources of Disentanglement in Variational Autoencoders
Advances in Neural Information Processing Systems 31 2018
Noisy Natural Gradient as Variational Inference
Proceedings of the 35th International Conference on Machine Learning (ICML) 2018
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
International Conference on Learning Representations (ICLR) 2018
Automatic chemical design using a data-driven continuous representation of molecules
American Chemical Society Central Science 2018
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Advances in Neural Information Processing Systems (NIPS) 2017
Reinterpreting Importance-Weighted Autoencoders
International Conference on Learning Representations (ICLR) Workshop Track 2017