Name: Topological Data Analysis for Neural Networks (en Inglés)
Price: 54.18 USD
Availability: InStock
Author: Ballester, Rubén; Casacuberta, Carles; Escalera, Sergio
ISBN: 9783032082824

portada Topological Data Analysis for Neural Networks (en Inglés)

Topological Data Analysis for Neural Networks (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Ballester, Rubén; Casacuberta, Carles; Escalera, Sergio

Idioma

Inglés

N° páginas

103

Encuadernación

Tapa Blanda

Dimensiones

23.6 x 15.7 x 0.8 cm

ISBN13

9783032082824

Topological Data Analysis for Neural Networks (en Inglés)

Ballester, Rubén; Casacuberta, Carles; Escalera, Sergio (Autor) · Springer · Tapa Blanda

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Reseña del libro "Topological Data Analysis for Neural Networks (en Inglés)"

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.