Discovering and harnessing symmetry with machine learning

Incorporating symmetry-inspired inductive biases into machine learning models has led to many significant advances in the field, especially for its application to scientific data. However, recently, a trend has emerged that favors implicitly learning relevant symmetries from data instead of designing constrained equivariant architectures. In this talk, I will first introduce these different modelling alternatives, together with their associated benefits and limitations. Then, I will describe some examples of automatic symmetry discovery methods as a way of mitigating some of those limitations. Finally, I will present our recent work that integrates symmetry discovery and the definition of an equivariant model into a joint learnable end-to-end approach, which further alleviates some of the limitations of current equivariant modelling approaches.