Roger Grosse

Faculty Member

Faculty Member

Associate Professor, Department Computer Science, University of Toronto

Canada CIFAR Artificial Intelligence Chair

Roger is an Assistant Professor of Computer Science at the University of Toronto, focusing on machine learning. Previously, he was a postdoc at the University of Toronto, after having received a Ph.D. at MIT, studying under Bill Freeman and Josh Tenenbaum. Before that, Roger did his undergraduate degree in symbolic systems and MS in computer science at Stanford University. Roger is a co-creator of Metacademy, a web site which uses a dependency graph of concepts to help you formulate personalized learning plans for machine learning and related topics.

Research Interests

  • AI Alignment
  • Understanding Deep Learning
  • Efficient Second-Order Approximations for Neural Nets

Highlights

  • Alfred P. Sloan Research Fellow in Computer Science
  • Connaught New Researcher Award
  • Canada Research Chair in Probabilistic Inference and Deep Learning
  • Canada CIFAR AI Chair

Publications

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Y. Wen, P. Vicol, J. Ba, D. Tran, and R. Grosse

International Conference on Learning Representations (ICLR) 2018

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

Y. Wu, M. Ren, R. Liao, and R. Grosse

International Conference on Learning Representations (ICLR) 2018

Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation

Y. Wu, E. Mansimov, R. B. Grosse, S. Liao, and J. Ba

Advances in Neural Information Processing Systems (NIPS) 2017

The reversible residual network: Backpropagation without storing activations

A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse

Advances in Neural Information Processing Systems (NIPS) 2017

Lime: Learning inductive bias for primitives of mathematical reasoning

Yuhuai Wu and Markus N Rabe and Wenda Li and Jimmy Ba and Roger B Grosse and Christian Szegedy

2021

Differentiable annealed importance sampling and the perils of gradient noise

Guodong Zhang and Kyle Hsu and Jianing Li and Chelsea Finn and Roger B Grosse

2021

On Monotonic Linear Interpolation of Neural Network Parameters

James R Lucas and Juhan Bae and Michael R Zhang and Stanislav Fort and Richard Zemel and Roger B Grosse

2021

REFACTOR: Learning to Extract Theorems from Proofs

Jin Peng Zhou and Yuhuai Wu and Qiyang Li and Roger Baker Grosse

2021

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds

Rob Brekelmans and Sicong Huang and Marzyeh Ghassemi and Greg Ver Steeg and Roger Baker Grosse and Alireza Makhzani

2021