Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks With Feedback

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Abstract

We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021), of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned. In Lynch and Mallmann-Trenn (Neural Networks, 2021), we considered simple tree-structured concepts and feed-forward layered networks. Here we extend the model in two ways: we allow limited overlap between children of different concepts, and we allow networks to include feedback edges. For these more general cases, we describe and analyze algorithms for recognition and algorithms for learning.
Original languageEnglish
Title of host publicationStructural Information and Communication Complexity - 30th International Colloquium, SIROCCO 2023, Proceedings
EditorsSergio Rajsbaum, Sergio Rajsbaum, Alkida Balliu, Dennis Olivetti, Joshua J. Daymude
Pages46-86
Number of pages41
DOIs
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13892 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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