ESE

In this talk, we explain different roles that symmetries and approximate symmetries can play in machine learning models. We define approximately equivariant graph neural networks and we show a bias-variance tradeoff when selecting the symmetries to enforce. We explain how to see equivariant functions as gradients of invariant functions, and we show how to use […]

Stochastic geometry is a branch of probability theory that deals with the study of random spatial patterns. Random point patterns, termed point processes, are the most basic such objects that appear in numerous applications. After presenting a brief introduction to point processes, we will present our work on the stochastic modeling and analysis of wireless […]