期刊全稱 | A Geometric Approach to the Unification of Symbolic Structures and Neural Networks | 影響因子2023 | Tiansi Dong | 視頻video | http://file.papertrans.cn/141/140874/140874.mp4 | 發(fā)行地址 | Presents a Geometric Approach to The Unification of Symbolic Structures and Neural Networks.Presents an up-to-date (as well as historical) look at the symbolic processing.Incorporates recent advances | 學科分類 | Studies in Computational Intelligence | 圖書封面 |  | 影響因子 | The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged. Unfortunately, present embedding approaches cannot. The approach in this book makes the unification possible. It is indeed a new and promising approach in AI. -Bo Zhang, Director of AI Institute, Tsinghua.It is indeed wonderful to see the reviving of the important theme Nural Symbolic Model. Given the popularity and prevalence of deep learning, symbolic processing is often neglected or downplayed. This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches. -Ron Sun (RPI), on Governing Board of Cognitive Science Society.Both for language and humor, approaches like those described in this book are the way to snickerdoodle wombats. -Christian F. Hempelmann (Texas A&M-Commerce) on Executive Board of International Society for Humor Studies. | Pindex | Book 2021 |
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