all AI news
RouteExplainer: An Explanation Framework for Vehicle Routing Problem
March 7, 2024, 5:41 a.m. | Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri, Yuusuke Nakano
cs.LG updates on arXiv.org arxiv.org
Abstract: The Vehicle Routing Problem (VRP) is a widely studied combinatorial optimization problem and has been applied to various practical problems. While the explainability for VRP is significant for improving the reliability and interactivity in practical VRP applications, it remains unexplored. In this paper, we propose RouteExplainer, a post-hoc explanation framework that explains the influence of each edge in a generated route. Our framework realizes this by rethinking a route as the sequence of actions and …
More from arxiv.org / cs.LG updates on arXiv.org
Training robust and generalizable quantum models
49 minutes ago |
arxiv.org
Causal Discovery Under Local Privacy
49 minutes ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Consultant Senior Power BI & Azure - CDI - H/F
@ Talan | Lyon, France