ESE Fall Seminar – “Approximate symmetries in machine learning”
Glandt Forum, Singh Center for Nanotechnology 3205 Walnut Street, Philadelphia, PA, United StatesIn 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 […]