| Description |
1 online resource (208 p.) |
| Contents |
Front Cover -- Mobile Edge Artificial Intelligence -- Copyright -- Contents -- List of figures -- Biography -- Yuanming Shi -- Kai Yang -- Zhanpeng Yang -- Yong Zhou -- Preface -- Acknowledgments -- Part 1 Introduction and overview -- 1 Motivations and organization -- 1.1 Motivations -- 1.2 Organization -- References -- 2 Primer on artificial intelligence -- 2.1 Basics of machine learning -- 2.1.1 Supervised learning -- 2.1.1.1 Logistic regression -- 2.1.1.2 Support vector machine -- 2.1.1.3 Decision tree -- 2.1.1.4 k-Nearest neighbors method -- 2.1.1.5 Neural network |
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2.1.2 Unsupervised learning -- 2.1.2.1 k-Means algorithm -- 2.1.2.2 Principal component analysis -- 2.1.2.3 Autoencoder -- 2.1.3 Reinforcement learning -- 2.1.3.1 Q-learning -- 2.1.3.2 Policy gradient -- 2.2 Models of deep learning -- 2.2.1 Convolutional neural network -- 2.2.2 Recurrent neural network -- 2.2.3 Graph neural network -- 2.2.4 Generative adversarial network -- 2.3 Summary -- References -- 3 Convex optimization -- 3.1 First-order methods -- 3.1.1 Gradient method for unconstrained problems -- 3.1.2 Gradient method for constrained problems -- 3.1.3 Subgradient descent method |
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3.1.4 Mirror descent method -- 3.1.5 Proximal gradient method -- 3.1.6 Accelerated gradient method -- 3.1.7 Smoothing for nonsmooth optimization -- 3.1.8 Dual and primal-dual methods -- 3.1.9 Alternating direction method of multipliers -- 3.1.10 Stochastic gradient method -- 3.2 Second-order methods -- 3.2.1 Newton's method -- 3.2.2 Quasi-Newton method -- 3.2.3 Gauss-Newton method -- 3.2.4 Natural gradient method -- 3.3 Summary -- References -- 4 Mobile edge AI -- 4.1 Overview -- 4.2 Edge inference -- 4.2.1 On-device inference -- 4.2.2 Edge inference via computation offloading |
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4.2.2.1 Server-based edge inference -- 4.2.2.2 Device-edge joint inference -- 4.3 Edge training -- 4.3.1 Data partition-based edge training -- 4.3.1.1 Distributed mode -- 4.3.1.2 Decentralized mode -- 4.3.2 Model partition-based edge training -- 4.4 Coded computing -- 4.5 Summary -- References -- Part 2 Edge inference -- 5 Model compression for on-device inference -- 5.1 Background on model compression -- 5.2 Layerwise network pruning -- 5.2.1 Problem statement -- 5.2.2 Convex approach for sparse objective and constraints -- 5.3 Nonconvex network pruning method with log-sum approximation |
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5.3.1 Log-sum approximation for sparse optimization -- 5.3.2 Iteratively reweighed minimization for log-sum approximation -- 5.4 Simulation results -- 5.4.1 Handwritten digits classification -- 5.4.2 Image classification -- 5.4.3 Keyword spotting inference -- 5.5 Summary -- References -- 6 Coded computing for on-device cooperative inference -- 6.1 Background on MapReduce -- 6.2 A communication-efficient data shuffling scheme -- 6.2.1 Communication model -- 6.2.2 Achievable data rates and DoF -- 6.3 A low-rank optimization framework for communication-efficient data shuffling |
| Notes |
Description based upon print version of record |
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6.3.1 Interference alignment conditions |
| Form |
Electronic book
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| Author |
Yang, Kai
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Yang, Zhanpeng
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Zhou, Yong
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| ISBN |
9780128238356 |
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0128238356 |
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