Artificial Intelligence for Edge Computing
| 企業作者: | |
|---|---|
| 其他作者: | , , |
| 總結: | XIV, 365 p. 113 illus., 98 illus. in color. text |
| 語言: | 英语 |
| 出版: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| 版: | 1st ed. 2023. |
| 主題: | |
| 在線閱讀: | https://doi.org/10.1007/978-3-031-40787-1 |
| 格式: | 電子 圖書 |
書本目錄:
- Part I: Core Problems
- Chapter 1: Neural Network Models for Time Series Data
- Chapter 2: Self-Supervised Learning from Unlabeled IoT Data
- Chapter 3: On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
- Chapter 4: Out of Distribution Detection
- Chapter 5: Model Compression for Edge Computing
- Part II: Distributed Problems
- Chapter 6: Communication Efficient Distributed Learning
- Chapter 7: Coreset-based Data Reduction for Machine Learning at the Edge
- Chapter 8: Lightweight Collaborative Perception at the Edge
- Chapter 9: Dynamic Placement of Services at the Edge
- Chapter 10: Joint Service Placement and Request Scheduling at the Edge
- Part III: Cross-cutting Thoughts
- Chapter 11: Criticality-based Data Segmentation and Resource Allocation in Machine Inference Pipelines
- Chapter 12: Model Operationalization at Edge Devices.