Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Learning with Limited Samples: Meta-Learning and Applications to Communication Systems

Learning with Limited Samples: Meta-Learning and Applications to Communication Systems

Chen, Lisha
Jose, Sharu Theresa
Nikoloska, Ivana

Paperback

Series: Foundations and Trends(r) in Signal Processing

Technology & Engineering

Currently unavailable to order

ISBN10: 163828136X
ISBN13: 9781638281368
Publisher: New Publ Inc
Published: Jan 25 2023
Pages: 144
Weight: 0.47
Height: 0.31 Width: 6.14 Depth: 9.21
Language: English
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are often difficult to translate into real-world engineering systems because deep learning models require a massive number of training samples, which are costly to obtain in practice. To address labeled data scarcity, few-shot meta-learning optimizes learning algorithms that can efficiently adapt to new tasks quickly. While meta-learning is gaining significant interest in the machine learning literature, its working principles and theoretic fundamentals are not as well understood in the engineering community. This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications. After introducing meta-learning in comparison with conventional and joint learning, the main meta-learning algorithms are described, as well as a general bilevel optimization framework for the definition of meta-learning techniques. Then, known results on the generalization capabilities of meta-learning from a statistical learning viewpoint are summarized. Applications to communication systems, including decoding and power allocation, are discussed next, followed by an introduction to aspects related to the integration of meta-learning with emerging computing technologies, namely neuromorphic and quantum computing. The monograph concludes with an overview of open research challenges.

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