Oct 16, 2023 · We introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through analytic matrix equivariant ...
Nov 22, 2023 · This paper proposes a new framework for Graph Neural Networks using the concept of Matrix Functions. They are good in modelling non-local ...
Jan 30, 2024 · We introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through analytic matrix equivariant ...
Apr 8, 2024 · We introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions through learnable equivariant matrix ...
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This repository contains the Matrix Function Network (MFN) implementation developed by Ilyes Batatia, Lars Schaaf, and Felix Faber.
To address these concerns, we introduce Matrix Function Neural Networks (MFNs), a novel architecture that parameterizes non-local interactions ...
May 18, 2023 · Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure.
A new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters that outperform ...
Jun 21, 2024 · Tess Smidt (MIT) https://simons.berkeley.edu/talks/tess-smidt-mit-2024-06-10 AI≡Science: Strengthening the Bond Between the Sciences and ...
Missing: Matrix Function