Interpretability in Deep Learning

Ayush Somani; Alexander Horsch; Dilip K. Prasad

doi:10.1007/978-3-031-20639-9

Interpretability in Deep Learning

Ayush Somani, Alexander Horsch, Dilip K. Prasad

UIT The Arctic University of Norway

Research output: Book/Report › Book › peer-review

4 Scopus citations

Abstract

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

Original language	English
Publisher	Springer International Publishing
Number of pages	466
ISBN (Electronic)	9783031206399
ISBN (Print)	9783031206382
DOIs	https://doi.org/10.1007/978-3-031-20639-9
State	Published - 1 Jan 2023
Externally published	Yes

Keywords

Black-box Nature of Deep Learning
Deep Learning
Explainable Artificial Intelligence
Explainable Deep Learning
Interpretability
Interpretable Learning
Knowledge Encoding in Deep Learning
Neural Networks

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10.1007/978-3-031-20639-9

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title = "Interpretability in Deep Learning",

abstract = "This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.",

keywords = "Black-box Nature of Deep Learning, Deep Learning, Explainable Artificial Intelligence, Explainable Deep Learning, Interpretability, Interpretable Learning, Knowledge Encoding in Deep Learning, Neural Networks",

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PY - 2023/1/1

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N2 - This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

AB - This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

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Interpretability in Deep Learning

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Keywords

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