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Found 6 papers, 1 papers with code
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems.
Inventing art styles with no artistic training data
We propose two procedures to create painting styles using models trained only on natural images, providing objective proof that the model is not plagiarizing human art styles.
Anti-symmetric Barron functions and their approximation with sums of determinants
A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles.
Convergence of variational Monte Carlo simulation and scale-invariant pre-training
We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem.
Efficient anti-symmetrization of a neural network layer by taming the sign problem
We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes.
