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

  • Tiansi Dong

Part of the Studies in Computational Intelligence book series (SCI, volume 910)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Tiansi Dong
    Pages 1-15
  3. Tiansi Dong
    Pages 105-116
  4. Tiansi Dong
    Pages 117-127
  5. Back Matter
    Pages 129-145

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


Symbolic Structures Neural Networks Symbolic Processing Geometric Connections Machine Symbolic Approaches

Authors and affiliations

  • Tiansi Dong
    • 1
  1. 1.ML2R Competence Center for Machine Learning Rhine-Ruhr, MLAI Lab, AI Foundations Group, Bonn-Aachen International Center for Information Technology (b-it)University of BonnBonnGermany

Bibliographic information

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