Lifelong Learning Machines – Approaches and Challenges

Soheil Kolouri

Vanderbilt University

Friday, October 28, 2022

11 AM – 12 PM CST

WebEx: https://utsa.webex.com/utsa/j.php?MTID=m0d75dea1a1670d0f72425cd9d59073ac

Abstract: The next wave of AI demands new machine learning frameworks that can continually learn and adapt to the stream of nonstationary multimodal information. This challenge is called continual, lifelong, or incremental learning in the ML community. Despite their phenomenal performance in various applications, deep neural networks are prone to forgetting their previously learned information upon learning new ones. This phenomenon is called “catastrophic forgetting” and is deeply rooted in the stability-plasticity dilemma. Overcoming catastrophic forgetting in deep neural networks has become an active field of research. In this talk, I will review some of our recent works on overcoming catastrophic forgetting in continual learning, from novel regularization-based approaches to sparsity and heterogeneous dropout. I conclude my talk by discussing our ongoing research on continual learning and some of the open problems and challenges.