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A geometric approach to the unificat...

Dong, Tiansi.

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A geometric approach to the unification of symbolic structures and neural networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A geometric approach to the unification of symbolic structures and neural networks/ by Tiansi Dong.
作者:	Dong, Tiansi.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xxii, 145 p. :ill., digital ;24 cm.
內容註:	Introduction -- The Gap between Symbolic and Connectionist Approaches -- Spatializing Symbolic Structures for the Gap -- The Criteria, Challenges, and the Back-Propagation Method -- Design Principles of Geometric Connectionist Machines -- A Geometric Connectionist Machine for Word-Senses -- Geometric Connectionist Machines for Triple Classiﬁcation -- Conclusions & Outlooks.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-030-56275-5
ISBN:	9783030562755

A geometric approach to the unification of symbolic structures and neural networks
Dong, Tiansi.

A geometric approach to the unification of symbolic structures and neural networks[electronic resource] /by Tiansi Dong. - Cham :Springer International Publishing :2021. - xxii, 145 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9101860-949X ;. - Studies in computational intelligence ;v.910..

Introduction -- The Gap between Symbolic and Connectionist Approaches -- Spatializing Symbolic Structures for the Gap -- The Criteria, Challenges, and the Back-Propagation Method -- Design Principles of Geometric Connectionist Machines -- A Geometric Connectionist Machine for Word-Senses -- Geometric Connectionist Machines for Triple Classiﬁcation -- Conclusions & Outlooks.

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.

ISBN: 9783030562755

Standard No.: 10.1007/978-3-030-56275-5doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .D66 2021

Dewey Class. No.: 006.32

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