Topological Data Analysis for Neural Networks

Name: Topological Data Analysis for Neural Networks
Brand: Springer-Verlag GmbH
SKU: 9783032082824
Price: 50.0 EUR
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Autor: Carles Casacuberta, Sergio Escalera

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer-Verlag GmbH

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This book offers a comprehensive presentation of methods from topological data analysis applied to t...

Informácie o knihe

Autor

Carles Casacuberta, Sergio Escalera

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2026

Stránok

103

EAN

9783032082824

ISBN

303208282X

Enbook ID

49598648

Vydavateľ

Springer-Verlag GmbH

Hmotnosť

190

Kompletný popis

This book offers a comprehensive presentation of methods from topological data analysis applied to the study of neural network structure and dynamics. Using topology-based tools such as persistent homology and the Mapper algorithm, the authors explore the intricate structures and behaviors of fully connected feedforward and convolutional neural networks.

The authors discuss various strategies for extracting topological information from data and neural networks, synthesizing insights and results from over 40 research articles, including their own contributions to the study of activations in complete neural network graphs. Furthermore, they examine how this topological information can be leveraged to analyze properties of neural networks such as their generalization capacity or expressivity. Practical implications of the use of topological data analysis in deep learning are also discussed, with a focus on areas including adversarial detection and model selection. The authors conclude with a summary of key insights along with a discussion of current challenges and potential future developments in the field.

This monograph is ideally suited for mathematicians with a background in topology who are interested in the applications of topological data analysis in artificial intelligence, as well as for computer scientists seeking to explore the practical use of topological tools in deep learning.

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Topological Data Analysis for Neural Networks

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