Introducing Information Measures via Inference [Lecture Notes]

Simeone, Osvaldo

doi:10.1109/msp.2017.2766239

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2018

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“…The latter is a measure of the distance between the two distributions, as we will further discuss in Sec. V-D (see [59], [60]). The analytical advantages of the ELBO L(q, θ) over the original log-likelihood are that: (i) it entails an expectation of the logarithm of the model p(x|z, θ), which, as mentioned, is typically a tractable function; and (ii) the average is over a fixed distribution q(z), which does not depend on the model parameter θ.…”

Section: Learningmentioning

confidence: 99%

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Simeone

2018

IEEE Trans. Cogn. Commun. Netw.

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435

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Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack, with an emphasis on the physical layer.

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Section: Learningmentioning

confidence: 99%