Integration-Free Kernels for Equivariant Gaussian Fields with Application in Dipole Moment Prediction
Abstract
We develop a Gaussian Process model for accurate prediction of the dipole moments of water molecules by incorporating their equivariance under rotations. While kernels guaranteeing such equivariances have been investigated in previous work, their evaluation is often computationaly prohibitive due to required integrations over the involved groups. In this work, we propose an alternative integration-free construction for equivariant kernels, relying on fundamental domain ideas previously explored in the scalar-valued invariant case, establishing a data-efficient and computationally lightweight GP model for dipole moments.
Cite
Text
Steinert et al. "Integration-Free Kernels for Equivariant Gaussian Fields with Application in Dipole Moment Prediction." NeurIPS 2024 Workshops: BDU, 2024.Markdown
[Steinert et al. "Integration-Free Kernels for Equivariant Gaussian Fields with Application in Dipole Moment Prediction." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/steinert2024neuripsw-integrationfree/)BibTeX
@inproceedings{steinert2024neuripsw-integrationfree,
title = {{Integration-Free Kernels for Equivariant Gaussian Fields with Application in Dipole Moment Prediction}},
author = {Steinert, Tim and Ginsbourger, David and Lykke-Møller, August Smart and Christiansen, Ove and Moss, Henry},
booktitle = {NeurIPS 2024 Workshops: BDU},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/steinert2024neuripsw-integrationfree/}
}