2 papers
cond-mat.mtrl-sci2026
Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning
Ashna Jose, Emilie Devijver, Martin Uhrin +2
Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples…
cs.LG2024
Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini +2
Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes cruci…