4 citations · 5 across the 15 of their papers we have counts for
4 papers · 1 filter
LEAP: Local ECT-Based Learnable Positional Encodings for Graphs
Juan Amboage, Ernst Röell, Patrick Schnider +1
Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet, standard message passing neural…
Molecular Machine Learning Using Euler Characteristic Transforms
Victor Toscano-Duran, Florian Rottach, Bastian Rieck
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. He…
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
Mathieu Alain, So Takao, Xiaowen Dong +2
Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs su…
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang, Lydia Mezrag, Xingzhi Sun +6
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learnin…