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  Subjects -> ELECTRONICS (Total: 207 journals)
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Foundations and Trends® in Signal Processing
Journal Prestige (SJR): 0.23
Citation Impact (citeScore): 2
Number of Followers: 7  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1932-8346 - ISSN (Online) 1932-8354
Published by Now Publishers Inc Homepage  [28 journals]
  • An Introduction to Quantum Machine Learning for Engineers

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      Abstract: AbstractIn the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parameterized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parameterized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parameterized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.Suggested CitationOsvaldo Simeone (2022), "An Introduction to Quantum Machine Learning for Engineers", Foundations and Trends® in Signal Processing: Vol. 16: No. 1-2, pp 1-223. http://dx.doi.org/10.1561/2000000118
      PubDate: Wed, 27 Jul 2022 00:00:00 +020
       
  • Wireless for Machine Learning: A Survey

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      Abstract: AbstractAs data generation increasingly takes place on devices without a wired connection, Machine Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this monograph, we give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives an introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.Suggested CitationHenrik Hellström, José Mairton B. da Silva Jr., Mohammad Mohammadi Amiri, Mingzhe Chen, Viktoria Fodor, H. Vincent Poor and Carlo Fischione (2022), "Wireless for Machine Learning: A Survey", Foundations and Trends® in Signal Processing: Vol. 15: No. 4, pp 290-399. http://dx.doi.org/10.1561/2000000114
      PubDate: Thu, 09 Jun 2022 00:00:00 +020
       
  • Bilevel Methods for Image Reconstruction

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      Abstract: AbstractThis review discusses methods for learning parameters for image reconstruction problems using bilevel formulations. Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions. Stateof- the-art image reconstruction methods learn these prior assumptions from training data using various machine learning techniques, such as bilevel methods.One can view the bilevel problem as formalizing hyperparameter optimization, as bridging machine learning and cost function based optimization methods, or as a method to learn variables best suited to a specific task. More formally, bilevel problems attempt to minimize an upper-level loss function, where variables in the upper-level loss function are themselves minimizers of a lower-level cost function.This review contains a running example problem of learning tuning parameters and the coefficients for sparsifying filters used in a regularizer. Such filters generalize the popular total variation regularization method, and learned filters are closely related to convolutional neural networks approaches that are rapidly gaining in popularity. Here, the lower-level problem is to reconstruct an image using a regularizer with learned sparsifying filters; the corresponding upper-level optimization problem involves a measure of reconstructed image quality based on training data.This review discusses multiple perspectives to motivate the use of bilevel methods and to make them more easily accessible to different audiences. We then turn to ways to optimize the bilevel problem, providing pros and cons of the variety of proposed approaches. Finally we overview bilevel applications in image reconstruction.Suggested CitationCaroline Crockett and Jeffrey A. Fessler (2022), "Bilevel Methods for Image Reconstruction", Foundations and Trends® in Signal Processing: Vol. 15: No. 2-3, pp 121-289. http://dx.doi.org/10.1561/2000000111
      PubDate: Thu, 05 May 2022 00:00:00 +020
       
 
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