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Found 3 papers, 1 papers with code
Sorting Out Quantum Monte Carlo
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 many particles.
Neural Conservation Laws: A Divergence-Free Perspective
We investigate the parameterization of deep neural networks that by design satisfy the continuity equation, a fundamental conservation law.
Input Convex Gradient Networks
The gradients of convex functions are expressive models of non-trivial vector fields.
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