88 citations · 96 across the 13 of their papers we have counts for
14 papers
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…
Designing Mechanical Meta-Materials by Learning Equivariant Flows
Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi +2
Mechanical meta-materials are solids whose geometric structure results in exotic nonlinear behaviors that are not typically achievable via homogeneous materials. We show how to dra…
Slow rates of approximation of U-statistics and V-statistics by quadratic forms of Gaussians
Kevin Han Huang, Peter Orbanz
We construct examples of degree-two U- and V-statistics of i.i.d.~heavy-tailed random vectors in , whose -th moments exist for , and provide tight…
Gaussian universality for approximately polynomial functions of high-dimensional data
Kevin Han Huang, Morgane Austern, Peter Orbanz
Gaussian universality results assert that the properties of many estimators remain unchanged when the input data are replaced by Gaussians. Such results have gained popularity in h…