2 papers
cs.LG2026
Interpretable Visualizations of Data Spaces for Classification Problems
Christian Jorgensen, Arthur Y. Lin, Rhushil Vasavada +1
How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes;…
cond-mat.soft2026
Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
Natalie E. Hooven, Arthur Y. Lin, Charles H. Carroll +1
Machine-Learning Interatomic Potentials (MLIPs) have surged in popularity due to their promise of expanding the spatiotemporal scales possible for simulating molecules with high fi…