3 papers
cs.LG2026
A Single Architecture for Representing Invariance Under Any Space Group
Cindy Y. Zhang, Elif Ertekin, Peter Orbanz +1
Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invarian…
cs.LG2025
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
Kevin Han Huang, Ni Zhan, Elif Ertekin +2
Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invaria…
cs.MS2025
Efficiently Vectorized MCMC on Modern Accelerators
Hugh Dance, Pierre Glaser, Peter Orbanz +1
With the advent of automatic vectorization tools (e.g., JAX's ), writing multi-chain MCMC algorithms is often now as simple as invoking those tools on single-chain c…