Gianluca Bencomo will present his MSE talk "Bayesian Filtering for Neural Networks" on Wednesday, April 26, 2023 at 9am in CS 401.

The members of his committee are as follows: Tom Griffiths (adviser) and Ryan Adams (reader).

All are welcome to attend.  Please see talk details below.

Title: Bayesian Filtering for Neural Networks

Abstract: Adaptability is a crucial component of intelligent systems. In dynamic environments, biological organisms continuously adjust their behavior to match the demands of the current situation. However, this fundamental feature of biological intelligence conflicts with a typical assumption of machine learning, which assumes that the data generating process is static over time. Bayesian filtering offers a rich toolkit for breaking this assumption, but the complex and high-dimensional space of neural networks poses several challenges. This thesis investigates these problems and presents progress for handling them. We demonstrate that human-like paradigms, such as continual learning and meta-learning, can be naturally interpreted within the Bayesian filtering framework. Furthermore, we show that sequential empirical risk minimization (ERM) is an implicit Bayesian filter, which explains its superior performance in time-varying prediction problems compared to more sophisticated methods. By making this implicit filter explicit, we can design machine learning systems that can more effectively adapt and generalize across time. Although this work is still in progress, this thesis provides a promising start to addressing this problem and sets the foundation for future research in this important domain.