Téléchargez le livre : Transparency and Interpretability for Learned Representations of Artificial Neural Networks

Transparency and Interpretability for Learned Representations of Artificial Neural Networks



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Éditeur :

Springer Vieweg


Paru le : 2022-11-26



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Description

Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI’s decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed lighton how to adopt an empirical neuroscience inspired approach to investigate a neural network’s learned representation in the same spirit as neuroscientific studies of the brain.
Pages
211 pages
Collection
n.c
Parution
2022-11-26
Marque
Springer Vieweg
EAN papier
9783658400033
EAN PDF
9783658400040
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
21
Taille du fichier
5530 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
84,39 €
EAN EPUB
9783658400040
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
21
Taille du fichier
40458 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
84,39 €

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