Overview
- 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 and new perspectives, thus leading to promising new methods and new approaches
Part of the book series: Studies in Computational Intelligence (SCI, volume 910)
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About this book
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
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
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
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Bibliographic Information
Book Title: A Geometric Approach to the Unification of Symbolic Structures and Neural Networks
Authors: Tiansi Dong
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-56275-5
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-56274-8Published: 25 August 2020
Softcover ISBN: 978-3-030-56277-9Published: 25 August 2021
eBook ISBN: 978-3-030-56275-5Published: 24 August 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXII, 145
Number of Illustrations: 103 b/w illustrations, 45 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Mathematical Models of Cognitive Processes and Neural Networks