Events

Deep Uncertainty

Speaker: Pascal Fua

Location: 60 Fifth Avenue, Room TBD

Date: Wednesday, August 16, 2023

Abstract

Among all the methods intended to estimate the uncertainty of deep network predictions, MC-Dropout and Deep Ensembles are the most widely used. The latter tend to deliver better estimates but at the cost of substantial computational and memory overheads, which makes them unsuitable for many real-world applications.In this talk, I will discuss alternative approaches that are designed to deliver the performance of Ensembles at a reduced computational cost by taking advantage of the regularities that can be found in the training data. In particular, I will show that when physics-based knowledge is available, the network can easily be engineered to exploit it and to deliver improved performance both in terms of accuracy and uncertainty estimation.

Bio

Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996 where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 300 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.