Artificial Intelligence for Edge Computing

Xehetasun bibliografikoak
Erakunde egilea:	SpringerLink (Online service)
Beste egile batzuk:	Srivatsa, Mudhakar (Argitaratzailea), Abdelzaher, Tarek (Argitaratzailea), He, Ting (Argitaratzailea)
Gaia:	XIV, 365 p. 113 illus., 98 illus. in color. text
Hizkuntza:	ingelesa
Argitaratua:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Edizioa:	1st ed. 2023.
Gaiak:	Computational intelligence. Robotics. Machine learning. Application software. Computer networks . Computational Intelligence. Machine Learning. Computer and Information Systems Applications. Computer Communication Networks.
Sarrera elektronikoa:	https://doi.org/10.1007/978-3-031-40787-1
Formatua:	Baliabide elektronikoa Liburua

Aurkibidea:

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.

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