IEEE Signal Processing Magazine

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Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation (RME) typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent RME methods are discussed.

Rethinking Bayesian Learning for Data Analysis: The art of prior and inference in sparsity-aware modeling

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Sparse modeling for signal processing and machine learning, in general, has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning (SAL) consists of two major paths paved by 1) discriminative methods that establish direct input–output mapping based on a regularized cost function optimization and 2) generative methods that learn the underlying distributions.

Artistic Text Style Transfer: An overview of state-of-the-art methods and datasets

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Word art, which is text rendered with properly designed appealing artistic effects, has been a popular form of art throughout human history. Artistic text effects are of great aesthetic value and symbolic significance. Decorating with appropriate effects not only makes text more attractive but also significantly enhances the atmosphere of a scene. Thus, artistic text effects are widely used in publicity and advertising.

Signal Processing at the Epicenter of Ground-Shaking Research: Researchers turn to signal processing to minimize earthquake damage, rescue victims, and perhaps even provide advance warnings

Special Reports

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Earthquakes have afflicted people throughout history. Today, thanks to advanced technology, more is known about earthquakes, and more can be done to protect people against them. Signal processing is playing a key role as investigators examine ways to combat one of humanity’s most deadly foes.

Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective

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In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex nonlinear learning models, such as deep neural networks. Gaining a better understanding is especially important, e.g., for safety-critical ML applications or medical diagnostics and so on. Although such explainable artificial intelligence (XAI) techniques have reached significant popularity for classifiers, thus far, little attention has been devoted to XAI for regression models (XAIR).

Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection

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In many modern data science problems, data are represented by a graph (network), e.g., social, biological, and communication networks. Over the past decade, numerous signal processing and machine learning (ML) algorithms have been introduced for analyzing graph structured data. With the growth of interest in graphs and graph-based learning tasks in a variety of applications, there is a need to explore explainability in graph data science.

Reproducibility in Matrix and Tensor Decompositions: Focus on model match, interpretability, and uniqueness

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Data-driven solutions are playing an increasingly important role in numerous practical problems across multiple disciplines. The shift from the traditional model-driven approaches to those that are data driven naturally emphasizes the importance of the explainability of solutions, as, in this case, the connection to a physical model is often not obvious. Explainability is a broad umbrella and includes interpretability, but it also implies that the solutions need to be complete, in that one should be able to “audit” them, ask appropriate questions, and hence gain further insight about their inner workings.

Interpretability, Reproducibility, and Replicability

Guest Editors

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Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction.

06 Jul

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Special Issue Deadlines

IEEE SPM Special Issue on 75th Anniversary of Signal Processing Society

White Paper Due: July 6, 2022
Publication Date: Mid 2023
CFP Document

Self-Supervised Representation Learning: Introduction, advances, and challenges

May 2022