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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…
cs.LG2025★ 1 cited
General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García +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…