Jason Li, Postdoctoral Fellow
Simons Institute, University of California, Berkeley
Eva Feng, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor David Toman
Greg Philbrick, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Craig Kaplan
This paper treats the subject of pseudo-3D modeling (via drawing in projective coordinates). I'll talk about the authors’ methods, as well as my own exploration of pseudo-3D drawing techniques.
Odunayo Ogundepo, Master’s candidate
David. R. Cheriton School of Computer Science
Supervisor: Professor Jimmy Lin
Pavel Izmailov, PhD candidate
Computer Science Department, New York University
Felix Dangel, Postdoctoral Researcher
Vector Institute for Artificial Intelligence
Popular deep learning frameworks prioritize computing the average mini-batch gradient. Yet, other quantities such as its variance or many approximations to the Hessian can be computed efficiently, and at the same time as the gradient mean. They are of great interest to researchers and practitioners, but implementing them is often burdensome or inefficient.
Anupa Murali, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Bin Ma
Niki Hasrati, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Shai Ben-David
Matt D’Souza, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ondřej Lhoták
Parametric polymorphism, also known as generics, is an abstraction that lets programmers define code that behaves independently of the types of values it operates on. Generics is a useful abstraction to enable code reuse and improve the maintainability of software projects.
Silvia Sellán, PhD candidate
Department of Computer Science, University of Toronto