Jesse Bettencourt

Graduate Student in Machine Learning

University of Toronto

Biography

I am a graduate student in Machine Learning at the University of Toronto and the Vector Institute. I am currently pursuing follow-up research to my work on Neural Ordinary Differential Equations, and am generally interested in approximate inference for latent variable models. I have recently completed an M.Sc. supervised by Drs. David Duvenaud and Roger Grosse, and am continuing as a Ph.D. student under David Duvenaud. In Winters 2018 and 2019 I was the instructor for CSC412/2506: Probabilistic Learning and Reasoning.

Winter 2020 I will be co-instructing, with David Duvenaud, the course STA414: Statistical Methods in Machine Learning II.

Interests

Neural ODEs
Approximate Inference
Automatic Differentiation

Education

PhD in Computer Science

University of Toronto, 2019 - Present
MSc in Computer Science

University of Toronto, 2017 - 2019
MSc in Mathematics

University of Toronto, 2015 - 2016
BSc in Integrated Science and Mathematics

McMaster Unviersity, 2011 - 2015

Publications

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Scaling up continuous normalizing flows by estimating the trace.

Will Grathwohl*, Ricky T. Q. Chen*, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

International Conference on Learning Representations, 2019.
Oral. Best Student Paper @ AABI 2018.

PDF Code Video

Neural Ordinary Differential Equations

A kind of continuous-depth Neural Network.

Ricky T. Q. Chen*, Yulia Rubanova*, Jesse Bettencourt*, David Duvenaud

Neural Information Processing Systems, 2018.
Oral. Best Paper Award.

PDF Code Poster Slides Video

* indicates equal contribution

Teaching

I am currently teaching the following course at University of Toronto:

STA414: Statistical Methods for Machine Learning II (Winter 2020)

In the past I have taught the following courses at University of Toronto:

CSC412/2506: Probabilistic Learning and Reasoning (Winter 2019)
CSC412/2506: Probabilistic Learning and Reasoning (Winter 2018)

In the past I have been a teaching assistant for the following courses:

CSC411/2515: Introduction to Machine Learning (Fall 2018)
CSC411/2515: Introduction to Machine Learning (Fall 2017)
MAT136: Single Variable Calculus I for Science (Winter 2016)
MAT186: Single Variable Calculus I for Engineering (Fall 2016)
MAT235: Mutlivariable and Vector Calculus II for Science (Summer 2016)
MAT235: Mutlivariable and Vector Calculus II for Science (Year 2016)
ISCI2A18: Mutlivariable and Vector Calculus II for Integrated Science (Year 2014)