21 citations · 41 across the 6 of their papers we have counts for
4 papers · 1 filter
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
Gil Goldshlager, Jiang Hu, Lin Lin
Subsampled natural gradient descent (SNG) has been used to enable high-precision scientific machine learning, but standard analyses based on stochastic preconditioning fail to prov…
Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
Andrés Guzmán-Cordero, Felix Dangel, Gil Goldshlager +1
Natural gradient methods significantly accelerate the training of Physics-Informed Neural Networks (PINNs), but are often prohibitively costly. We introduce a suite of techniques t…
Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing
Gil Goldshlager, Jiang Hu, Lin Lin
Due to the ever growing amounts of data leveraged for machine learning and scientific computing, it is increasingly important to develop algorithms that sample only a small portion…
Convergence of variational Monte Carlo simulation and scale-invariant pre-training
Nilin Abrahamsen, Zhiyan Ding, Gil Goldshlager +1
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. We…