Geometry, Algebra and Physics in Deep Neural Networks

The research group on Geometry, Algebra and Physics in Deep Neural Networks (GAPinDNNs) is based at the Department for Mathematical Sciences at Chalmers University of Technology and the University of Gothenburg. Our vision is to develop a mathematical foundation for deep learning which elevates the field into a theoretically well-grounded science.

News

Princeton Machine Learning Theory Summer School
13 Aug 2024

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.

IAFI 2024 Summer Workshop
12 Aug 2024

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.

Master Thesis Defense
02 Jul 2024

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

CVPR 2024 in Seattle
21 Jun 2024

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.

Equivariant Neural Networks

Theory and applications of quivariant neural networks and geometric deep learning

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Spherical Computer Vision

Computer vision models for spherical data like fisheye images

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Wide Neural Networks

Theory and applications of wide neural networks, using the neural tangent kernel

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