Seminar • Artificial Intelligence | Natural Language Processing — Learning General Language Processing Agents
Please note: This seminar will be given online.
Dani Yogatama, Research Scientist
DeepMind
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.
Florian Tramèr, Computer Science Department
Stanford University
Failures of machine learning systems can threaten both the security and privacy of their users. My research studies these failures from an adversarial perspective, by building new attacks that highlight critical vulnerabilities in the machine learning pipeline, and designing new defenses that protect users against identified threats.
Weihao Kong, Postdoctoral researcher
Department of Computer Science, University of Washington
In this talk, I will discuss several examples of my research that reveal a surprising ability to extract accurate information from modest amounts of data.
Ryan Goldade, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Christopher Batty