Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances / by Yanan Sun, Gary G. Yen, Mengjie Zhang.

Format:

Book

Author/Creator:

Sun, Yanan, author.

Yen, Gary G., author.

Zhang, Mengjie, author.

Series:

Studies in Computational Intelligence, 1860-9503 ; 1070

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Computational Intelligence.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Physical Description:

1 online resource (335 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

Summary:

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

Contents:

Part I: Fundamentals and Backgrounds

Evolutionary Computation

Deep Neural Networks

Part II: Evolutionary Deep Neural Architecture Search for Unsupervised DNNs

Architecture Design for Stacked AEs and DBNs

Architecture Design for Convolutional Auto-Encoders

Architecture Design for Variational Auto-Encoders

Part III: Evolutionary Deep Neural Architecture Search for Supervised DNNs

Architecture Design for Plain CNNs

Architecture Design for RBs and DBs Based CNNs

Architecture Design for Skip-Connection Based CNNs

Hybrid GA and PSO for Architecture Design

Internet Protocol Based Architecture Design

Differential Evolution for Architecture Design

Architecture Design for Analyzing Hyperspectral Images

Part IV: Recent Advances in Evolutionary Deep Neural Architecture Search

Encoding Space Based on Directed Acyclic Graphs

End-to-End Performance Predictors

Deep Neural Architecture Pruning

Deep Neural Architecture Compression

Distribution Training Framework for Architecture Design.

Notes:

Includes bibliographical references.

ISBN:

9783031168680

3031168682

OCLC:

1350687879