The main goal of my research is to get machine learning methods to work well with considerably less labeled data.
To accomplish this goal I am interested in semi-supervised learning methods as well as incorporating (mainly discrete) structure into inference models.
As of Febuary 2019, I also work part-time at Google Brain in Toronto.
My last paper FFJORD was just selected for an oral presentation at ICLR 2019! (top 1.5% of submissions)
Modeling Global Class Structure Leads to Rapid Integration of New Classes: Will Grathwohl, Eleni Triantafillou, Xuechen Li, David Duvenaud and Richard Zemel.
NIPS 2018 Workshop on Meta-Learning
NIPS 2018 Workshop on Continual Learning
Training Glow with Constant Memory Cost: Xuechen Li, Will Grathwohl.
NIPS 2018 Workshop on Bayesian Deep Learning
Gradient-Based Optimization of Neural Network Architecture: Will Grathwohl, Elliot Creager, Kamyar Ghasemipour, Richard Zemel.
ICLR 2018 Workshop.
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation: Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud.
NIPS 2017 Deep Reinforcement Learning Symposium. Oral Presentation. Video of my talk found here.
Borealis AI Graduate Fellowship: A $50,000, 2 year fellowship funding research in AI. Funded by the Royal Bank of Canada. Huawei Prize: A financial award based on academic and research performance. ICLR 2018 Travel Award Best Paper Award: Symposium on Advances in Approximate Bayesian Inference 2018