4 papers
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…
Neural Point-Forms
Bruno Trentini, Jacob Hume, Vincenzo Antonio Isoldi +3
Point cloud learning often rests on the premise that observed samples are noisy traces of an underlying geometric object, such as a manifold embedded in a high-dimensional feature…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
Equivariant Neural Tangent Kernels
Philipp Misof, Pan Kessel, Jan E. Gerken
Little is known about the training dynamics of equivariant neural networks, in particular how it compares to data augmented training of their non-equivariant counterparts. Recently…