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Section: Events
Date: June 24, 2022
Time: 12:00 PM ET (New York Time)
Title: Fairness-aware Learning Over Networks
Registration | Full webinar details
Section: Events
August 5-6, 2022
Location: Hybrid
Section: Events
Submission Deadline: 1 February 2023
Call for Proposals Document
Section: Publications & Resources
The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.
Section: Publications & Resources
The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.
Section: Publications & Resources
The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
The July issue of IEEE Signal Processing Magazine (SPM) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive papers.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
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.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
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.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
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.
