5 papers
Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing
Lukas Fesser, Melanie Weber
Graph Neural Networks have emerged as the most popular architecture for graph-level learning, including graph classification and regression tasks, which frequently arise in areas s…
Enhancing the Utility of Higher-Order Information in Relational Learning
Raphael Pellegrin, Lukas Fesser, Melanie Weber
Higher-order information is crucial for relational learning in many domains where relationships extend beyond pairwise interactions. Hypergraphs provide a natural framework for mod…
Multimodal Medical Code Tokenizer
Xiaorui Su, Shvat Messica, Yepeng Huang +5
Foundation models trained on patient electronic health records (EHRs) require tokenizing medical data into sequences of discrete vocabulary items. Existing tokenizers treat medical…
Unitary convolutions for learning on graphs and groups
Bobak T. Kiani, Lukas Fesser, Melanie Weber
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-inva…
Tightness of Bernoulli Gibbsian line ensembles
Evgeni Dimitrov, Xiang Fang, Lukas Fesser +4
A Bernoulli Gibbsian line ensemble is the law of the trajectories of independent Bernoulli random walkers with possib…