Titlebook: Artificial Intelligence for Edge Computing; Mudhakar Srivatsa,Tarek Abdelzaher,Ting He Book 2023 The Editor(s) (if applicable) and The Aut

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poliosis

On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Modelsneural network with ReLU activation that has no bias term. We show that, depending on the ground-truth function, the test error of overfitted NTK models exhibits characteristics that are different from the “double-descent” of other overparameterized linear models with simple Fourier or Gaussian feat

領(lǐng)先

Out of Distribution Detectionable to out of distribution detection (OOD) due to some unique characteristics of anomalies. OOD records are rare, heterogeneous, boundless, and prohibitively high costs for collecting large-scale OOD data. OOD records leads to false predictions for AI models. It reduces user confidence in AI produc

nonplus

Model Compression for Edge Computingd resources of edge devices. Many traditional AI models are designed for large-scale cloud environments with ample GPUs. The computational environment at the edge is substantially different. Specifically, it is much more resource-constrained. Fortunately, often edge applications are also more restri

獸群

Communication Efficient Distributed Learningal approaches have been proposed to mitigate this issue, using gradient compression and infrequent communication based techniques. This chapter summarizes two communication efficient algorithms, . and ., for . and . settings, respectively. These algorithms utilize . sparsification and quantization o

有限

Coreset-Based Data Reduction for Machine Learning at the Edgeditional data compression schemes that aim at supporting the reconstruction of the original data, here the compression only needs to support the learning of the models that need to be learned from the original data, in order to support AI applications in a bandwidth-limited edge network. This lowere

溫順

Lightweight Collaborative Perception at the Edges are optimized jointly to overcome both computational and communication resource constraints. Collaborative Edge Perception exploits the fact that multiple sensor nodes often observe the same physical phenomena and/or the same objects, but from different spatial perspectives and/or at different ins

HIKE

Dynamic Placement of Services at the Edgets service may need to be migrated to a new location. In this chapter, we first formulate this migration decision-making problem as a Markov decision process (MDP). Then, by analyzing the characteristics of this MDP, we provide efficient ways of obtaining the near-optimal policy for service migratio

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Joint Service Placement and Request Scheduling at the Edgems. To have the maximum applicability, the machine learning workloads will be simply modeled as demands for various types of resources (storage, communication, computation), and the resource allocation algorithms are designed to optimally satisfy these demands within the limited resource capacities

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