Continual General Chunking Problem and SyncMap

Danilo Vasconcellos Vargas, Toshitake Asabuki

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

Humans possess an inherent ability to chunk sequences into their constituent parts. In fact, this ability is thought to bootstrap language skills and learning of image patterns which might be a key to a more animal-like type of intelligence. Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations. Additionally, we propose an algorithm called SyncMap that can learn and adapt to changes in the problem by creating a dynamic map which preserves the correlation between variables. Results of SyncMap suggest that the proposed algorithm learn near optimal solutions, despite the presence of many types of structures and their continual variation. When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on 66% of the scenarios while being the second best in the remaining 34%. SyncMap’s model-free simple dynamics and the absence of loss functions reveal that, perhaps surprisingly, much can be done with self-organization alone.

本文言語英語
ホスト出版物のタイトル35th AAAI Conference on Artificial Intelligence, AAAI 2021
出版社Association for the Advancement of Artificial Intelligence
ページ10006-10014
ページ数9
ISBN(電子版)9781713835974
出版ステータス出版済み - 2021
イベント35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
継続期間: 2月 2 20212月 9 2021

出版物シリーズ

名前35th AAAI Conference on Artificial Intelligence, AAAI 2021
11B

会議

会議35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/2/212/9/21

!!!All Science Journal Classification (ASJC) codes

  • 人工知能

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