3 papers
cs.LG2026
GEDAN: Learning the Edit Costs for Graph Edit Distance
Francesco Leonardi, Markus Orsi, Jean-Louis Reymond +1
Graph Edit Distance (GED) is defined as the minimum cost transformation of one graph into another and is a widely adopted metric for measuring the dissimilarity between graphs. The…
cs.LG2026
MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields
Francesco Leonardi, Boris Bonev, Kaspar Riesen
Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angul…
cs.LG2024
Neural Decompiling of Tracr Transformers
Hannes Thurnherr, Kaspar Riesen
Recently, the transformer architecture has enabled substantial progress in many areas of pattern recognition and machine learning. However, as with other neural network models, the…