5 citations · 7 across the 6 of their papers we have counts for
3 papers · 1 filter
Neural Low-Discrepancy Sequences
Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine +2
Low-discrepancy points are designed to efficiently fill the space in a uniform manner. This uniformity is highly advantageous in many problems in science and engineering, including…
On the optimization of discrepancy measures
François Clément, Nathan Kirk, Art B. Owen +1
Numerical optimizations can find point sets with lower discrepancies than prior methods based on algebra, and discrepancies are computationally attractive for this. The …
Low Stein Discrepancy via Message-Passing Monte Carlo
Nathan Kirk, T. Konstantin Rusch, Jakob Zech +1
Message-Passing Monte Carlo (MPMC) was recently introduced as a novel low-discrepancy sampling approach leveraging tools from geometric deep learning. While originally designed for…