10 citations · 11 across the 3 of their papers we have counts for
3 papers
Sorting Out Quantum Monte Carlo
Jack Richter-Powell, Luca Thiede, Alán Asparu-Guzik +1
Molecular modeling at the quantum level requires choosing a parameterization of the wavefunction that both respects the required particle symmetries, and is scalable to systems of…
Neural Conservation Laws: A Divergence-Free Perspective
Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen
We investigate the parameterization of deep neural networks that by design satisfy the continuity equation, a fundamental conservation law. This is enabled by the observation that…
Input Convex Gradient Networks
Jack Richter-Powell, Jonathan Lorraine, Brandon Amos
The gradients of convex functions are expressive models of non-trivial vector fields. For example, Brenier's theorem yields that the optimal transport map between any two measures…