16 citations · 25 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Graph-Based Offline Signature Verification
Paul Maergner, Nicholas R. Howe, Kaspar Riesen +2
Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to sig…
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
Paul Maergner, Vinaychandran Pondenkandath, Michele Alberti +4
Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures.…