IEEE

[Seminar] EE : 2 November 2018: Fast and Scalable Estimation of Uncertainty using Bayesian Deep Learning

Department of Electrical Engineering
and
IEEE Signal Processing Society Bangalore Chapter

invite you to a talk by

Dr. Emtiyaz Khan, RIKEN Center for Advanced Intelligence Project, Tokyo

on

Fast and Scalable Estimation of Uncertainty using Bayesian Deep Learning

November 2, 2018; 2.30 PM, Multimedia Classroom, Electrical Engineering department, IISc

Abstract:

Uncertainty estimation is essential to design robust and reliable systems, but this usually requires more effort to implement and execute compared to maximum-likelihood methods. In this talk, I will summarize some of our recent work that enables fast and scalable estimation of uncertainty using deep models, such as Bayesian neural network. The main feature of our method is that they are extremely easy to implement within existing deep-learning softwares. I will also summarize some of the current challenges faced by the Bayesian deep-learning community and how real-world applications can be useful for our research.

Joint work with Wu Lin (UBC), Didrik Nielsen (RIKEN), Voot Tangkaratt (RIKEN), Yarin Gal (UOxford), Akash Srivastva (UEdinburgh), Zuozhu Liu (SUTD).

About the speaker:
Dr. Emtiyaz Khan is a team leader (equivalent to Full Professor) at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference (ABI) Team. Since April 2018, he is a visiting professor at the EE department in Tokyo University of Agriculture and Technology (TUAT) and also a part-time lecturer at Waseda University.

===================================
Co-sponsor:
IEEE Signal Processing Society
Bangalore chapter
===================================

Leave a Reply