88 citations · 96 across the 13 of their papers we have counts for
4 papers · 1 filter
Coordinate-Free Automatic Differentiation
Alex Guerra, Joshua Aduol, Yaqian Tang +3
Automatic differentiation (AD) is a cornerstone of modern machine learning. Since all AD frameworks implicitly fix a basis of Euclidean space to define gradients and Jacobians, the…
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…
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…
Gaussian and Non-Gaussian Universality of Data Augmentation
Kevin Han Huang, Peter Orbanz, Morgane Austern
We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several speci…