Virtual Seminar - Modeling Uncertainty in Learning with Little Data

Seminar
Thursday, May 07, 2020
11:00am - 12:00pm
Online

Few-shot classification, the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. I will present what I think are some of the key advances and open questions in this area. I will then focus on the fundamental issue of overfitting in the few-shot scenario. Bayesian methods are well-suited to tackling this issue because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of Pólya-gamma augmentation and the one-vs-each loss that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.

 

The seminar was delivered live using Zoom on 5/7/20. You can watch a recording of the talk on the WNCG YouTube channel here.

Speaker

Photo: Prof. Richard Zemel
Professor
University of Toronto

Richard Zemel is a Professor of Computer Science at the University of Toronto, where he has been a faculty member since 2000. Prior to that, he was an Assistant Professor in Computer Science and Psychology at the University of Arizona and a Postdoctoral Fellow at the Salk Institute and at Carnegie Mellon University. He received a B.Sc. degree in History & Science from Harvard University in 1984 and a Ph.D. in Computer Science from the University of Toronto in 1993. He is also the co-founder of SmartFinance, a financial technology startup specializing in data enrichment and natural language processing.

His awards include an NVIDIA Pioneers of AI Award, a Young Investigator Award from the Office of Naval Research, a Presidential Scholar Award, two NSERC Discovery Accelerators, and seven Dean’s Excellence Awards at the University of Toronto. He is a Fellow of the Canadian Institute for Advanced Research and is on the Executive Board of the Neural Information Processing Society, which runs the premier international machine learning conference.

His research contributions include foundational work on systems that learn useful representations of data without any supervision; methods for learning to rank and recommend items; and machine learning systems for automatic captioning and answering questions about images. He developed the Toronto Paper Matching System, a a system for matching paper submissions to reviewers, which is being used in many conferences, including NIPS, ICML, CVPR, ICCV, and UAI. His research is supported by grants from NSERC, CIFAR, Microsoft, Google, Samsung, DARPA and iARPA.