Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances
| Hauptverfasser: | , , |
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| Körperschaft: | |
| Zusammenfassung: | XVI, 331 p. 91 illus., 77 illus. in color. text |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Ausgabe: | 1st ed. 2023. |
| Schriftenreihe: | Studies in Computational Intelligence,
1070 |
| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.1007/978-3-031-16868-0 |
| Format: | Elektronisch Buch |
Inhaltsangabe:
- 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.