3 papers
cs.LG2026
Reducing cross-sample prediction churn in scientific machine learning
Gordan Prastalo, Kevin Maik Jablonka
Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training data. Across chemistry ben…
physics.chem-ph2026
Beyond Learning on Molecules by Weakly Supervising on Molecules
Gordan Prastalo, Kevin Maik Jablonka
Molecular representations are inherently task-dependent, yet most pre-trained molecular encoders are not. Task conditioning promises representations that reorganize based on task d…
cs.LG2025
General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Nawaf Alampara, Anagha Aneesh, Martiño RÃos-GarcÃa +6
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, f…