{"type":"ScholarlyArticle","title":"Combinatorial Routing for Neural Trees","authors":[{"name":"Jiahao Li","slug":"jiahao-li","given":"Jiahao","family":"Li","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"},{"name":"Ruichu Cai","slug":"ruichu-cai","given":"Ruichu","family":"Cai","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"},{"name":"Yuguang Yan","slug":"yuguang-yan","given":"Yuguang","family":"Yan","affiliation":"School of Computer Science , Guangdong University of Technology , Guangzhou , China"}],"abstract":"Neural trees benefit from the high-level representation of neural networks and the interpretability of decision trees. Therefore, the existing works on neural trees perform outstandingly on various tasks such as architecture search. However, these works require every router to provide only one successor for each sample, causing the predictions to be dominated by the elite branch and its derivative architectures. To break this branch dominance, we propose the combinatorial routing neural tree method, termed CombRo. Unlike the previous methods employing unicast routing, CombRo performs multicast schema in each iteration, allowing the features to be routed to any combination of successors at every non-leaf. The weights of each architecture are then evaluated accordingly. We update the weights by training the routing subnetwork, and the architecture with the top weight is selected in the final step. We compare CombRo with the existing algorithms on 3 public image datasets, demonstrating its superior performance in terms of accuracy. Visualization results further validate the effectiveness of the multicast routing schema. Code is available at https://github.com/JiahaoLi-gdut/CombRo.","doi":"10.24963/ijcai.2024/487","url":"https://ijcai.org/proceedings/2024/487","pdf":"https://ijcai.org/proceedings/2024/0487.pdf","markdown":"https://ijcai.org/proceedings/2024/487.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/487","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":487,"pages":{"first":4407,"last":4415},"keywords":["Machine Learning: ML: Deep learning architectures","Computer Vision: CV: Machine learning for vision","Machine Learning: ML: Classification","Machine Learning: ML: Ensemble methods"],"videos":[],"references":[{"position":1,"raw":"[Aboah, 2021] Armstrong Aboah. 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