Speaker
Prof.
Jan Gerken
Description
Symmetries are of fundamental importance in all of science and therefore critical for the success of deep learning systems used in this domain.
In this talk, I will give an overview of the different forms in which symmetries appear in physics and chemistry and explain the theoretical background behind equivariant neural networks.
Then, I will discuss common ways of constructing equivariant networks in different settings and contrast manifestly equivariant networks with other techniques for reaching equivariant models.
Finally, I will report on recent results about the symmetry properties of deep ensembles trained with data augmentation.
