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A Geometric Approach to the Unification of Symbolic Structures and Neural Networks de Tiansi Dong

A Geometric Approach to the Unification of Symbolic Structures and Neural Networks: Studies in Computational Intelligence, cartea 910

Autor Tiansi Dong
en Limba Engleză Paperback – 25 aug 2021
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
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Specificații

ISBN-13: 9783030562779
ISBN-10: 3030562778
Ilustrații: XXII, 145 p. 148 illus., 45 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.25 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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

Caracteristici

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