Automated Deep Learning : Neural Architecture Search Is Not the End (Foundations and Trends® in Machine Learning)

Dong, Xuanyi/ Kedziora, David Jacob/ Musial, Katarzyna

now publishers Inc（2024/02発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 166 p.
• 言語 ENG
• 商品コード 9781638283188

Full Description

Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation. Automated deep learning (AutoDL) endeavours to minimize the need for human involvement and is best known for its achievements in neural architecture search (NAS).

In this monograph, the authors examine research efforts into automation across the entirety of an archetypal DL workflow. In so doing, they propose a comprehensive set of ten criteria by which to assess existing work in both individual publications and broader research areas, namely novelty, solution quality, efficiency, stability, interpretability, reproducibility, engineering quality, scalability, generalizability, and eco-friendliness.

Aimed at students and researchers, this monograph provides an evaluative overview of AutoDL in the early 2020s, identifying where future opportunities for progress may exist.

Contents

1. Introduction
2. AutoDL: An Overview
3. Automated Problem Formulation
4. Automated Data Engineering
5. Neural Architecture Search
6. Hyperparameter Optimization
7. Automated Deployment
8. Automated Maintenance
9. Critical Discussion and Future Directions
10. Conclusions
Acknowledgments
References