3 papers
cs.LG2026
SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests…
cs.LG2025
Graph Mixing Additive Networks
Maya Bechler-Speicher, Andrea Zerio, Maor Huri +5
We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN repre…
q-bio.QM2025
Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
Andrea Zerio, Maya Bechler-Speicher, Tine Jess +1
Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular sampling and informative observa…