A Geometric Approach to the Unification of Symbolic Structures and Neural Networks
Author:   Tiansi Dong
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   910
ISBN:  

9783030562779


Pages:   145
Publication Date:   25 August 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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A Geometric Approach to the Unification of Symbolic Structures and Neural Networks


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Overview

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

Full Product Details

Author:   Tiansi Dong
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   910
Weight:   0.267kg
ISBN:  

9783030562779


ISBN 10:   3030562778
Pages:   145
Publication Date:   25 August 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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