Jan Gerken
I lead a WASP-funded group at the division of Algebra and Geometry in the area of mathematical foundations of deep learning. Our main focus lies on equivariant- and wide neural networks. On the theory side, our work is heavily inspired by physics, borrowing from concepts like gauge theory, general relativity and quantum field theory. On the practical side, we work on applications in condensed matter physics, quantum chemistry and computer vision.
Showing 4 publications
Diffeomorphic Counterfactuals with Generative Models
Geometric deep learning and equivariant neural networks
Equivariance versus Augmentation for Spherical Images
Towards closed strings as single-valued open strings at genus one
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