42 citations · 165 across the 19 of their papers we have counts for
3 papers · 1 filter
Finite-Width Neural Tangent Kernels from Feynman Diagrams
Max Guillen, Philipp Misof, Jan E. Gerken
Neural tangent kernels (NTKs) are a powerful tool for analyzing deep, non-linear neural networks. In the infinite-width limit, NTKs can easily be computed for most common architect…
PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Tage Tykesson, Pietro Rosso +3
Artificial intelligence is rapidly reshaping the natural sciences, with weather forecasting emerging as a flagship AI4Science application where machine learning models can now riva…
Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks
Longde Huang, Oleksandr Balabanov, Hampus Linander +3
Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context f…