| Título |
Graph Representation for Learning the Traveling Salesman Problem |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
13th Mexican Conference on Pattern Recognition, MCPR 2021 |
| Resumen |
Training deep learning models for solving the Travelling Salesman Problem (TSP) directly on large instances is computationally challenging. An approach to tackle large-scale TSPs is through identify- ing elements in the model or training procedure that promotes out-of- distribution (OoD) generalization, i.e., generalization to samples larger than those seen in training. The state-of-the-art TSP solvers based on Graph Neural Networks (GNNs) follow different strategies to represent the TSP instances as input graphs. In this paper, we conduct exper- iments comparing different graph representations finding features that lead to a better OoD generalization. |
| Observaciones |
DOI 10.1007/978-3-030-77004-4_15
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|
| Lugar |
Virtual, online |
| País |
Indefinido |
| No. de páginas |
153-162 |
| Vol. / Cap. |
v. 12725 LNCS |
| Inicio |
2021-06-23 |
| Fin |
2021-06-26 |
| ISBN/ISSN |
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