Geometry, Algebra and Physics in Deep Neural Networks

Public lecture by Daniel Persson for high school students at Hulebäcksgymnasiet.

Paper by Daniel Persson together with Emma Andersdotter and Fredrik Ohlsson at Umeå University.

This week Elias Nyholm is attending the CaLISTA workshop on Geometry-Informed Machine Learning in Paris, where he will present a poster on General Equivariant Transformers.

Daniel Persson organized a workshop at Hässelby slott in Stockholm, aimed at gathering all supervisors within the WASP math-AI track. Jan Gerken gave a talk on Geometric deep learning and neural tangent kernels.

Philipp Misof is currently at the Princeton University and meeting other PhD students and lecturers focusing on theoretical aspects of ML. Today he will present his poster on “Equivariant Neural Tangent Kernels” at the poster session.

Our group members Max Guillen and Jan Gerken participated in the IAIFI 2024 Summer Workshop about physics and AI at MIT and presented work on the RG flow of the NTK dynamics at finite-width from Feynman diagrams as well as Symmetries and the Neural Tangent Kernel.

William Nyrén and Ibrahim Taha defended their master thesis on “Predicting UV-Vis absorption spectra by using graph neural network models”. Congratulations!

Several members of our group, Oscar Carlsson, Jan Gerken, Hampus Linander and Christoffer Petersson traveled to Seattle to participate in CVPR 2024. We had an inspiring conference, met many new and old colleagues and presented our work on HEAL-SWIN: A Vision Transformer on The Sphere.

Thanks to all the speakers and participants of this interesting workshop at the Umeå University which was coorganized by our colleague Jan Gerken. We had a broad range of talks and discussions focusing on geometric aspects of deep learning.

The paper “Emergent Equivariance in Deep Ensembles” from our group was accepted for an oral presentation at ICML 2024. Congratulations Jan Gerken and Pan Kessel!

The preprint “Equivariant Neural Tangent Kernels” from our group appeared on the arXiv today. We compute neural tangent kernels (NTKs) for group convolutional networks for the first time and show that equivariant NTKs outperform their non-equivariant counterparts on a medical image dataset.