Andrew Quinn, Electrical Engineering and Computer Science Department
University of Michigan
Wenhu Chen, Department of Computer Science
University of California, Santa Barbara
Mustafa Korkmaz, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ken Salem
Dani Yogatama, Research Scientist
DeepMind
Xiang Fang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Stephen Mann
A new set of schemes are designed to create smooth surfaces with continuous curvatures or higher order continuity for triangular scattered data sites, without complex computation.
Ellen Vitercik, Computer Science Department
Carnegie Mellon University
Georgios Michalopoulos, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Ian McKillop and Helen Chen
Mengye Ren, Department of Computer Science
University of Toronto
Over the past decades, we have seen machine learning making great strides in AI applications. Yet, most of its success relies on training models offline on a massive amount of data and evaluating them in a similar test environment. By contrast, humans can learn new concepts and skills with very few examples, and can easily generalize to novel tasks.
Wei Hu, Department of Computer Science
Princeton University
Akshay Ramachandran, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Lap Chi Lau
The matrix normal model, the family of Gaussian matrix-variate distributions whose covariance matrix is the Kronecker product of two lower dimensional factors, is frequently used to model matrix-variate data. The tensor normal model generalizes this family to Kronecker products of three or more factors.