4 accepted papers
Regression is a cornerstone of statistics and machine learning, with applications spanning science, engineering, and economics. While quantum algorithms for regression have attracted considerable attention, most existing work has focused on linear regression, leaving many more complex yet practicall…
Graph sparsification serves as a foundation for many algorithms, such as approximation algorithms for graph cuts and Laplacian system solvers. As its natural generalization, hypergraph sparsification has recently gained increasing attention, with broad applications in graph machine learning and othe…
Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specific…
The reactor power and the coolant level in the nuclear plant are monitored via the reactor core detectors. Every 4 to 5 years, the detectors with high-level radiation need to be removed, which is time-consuming and hazardous for workers. To address this issue, this paper introduces a novel robotic s…