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The Latest News, Articles, and Events in Signal Processing
Submission Deadline: February 19, 2021
Call for Proposals Document
This issue brings to you our interview with Dr. Ervin Sejdić, an Associate Professor at the Swanson School of Engineering, University of Pittsburgh, where he directs a research laboratory focused on engineering developments in medicine. His research has focused on creating computational biomarkers indicative of age- and disease-related changes in functional outcomes such as swallowing, gait, and handwriting.
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.
The IEEE Signal Processing Society (SPS) would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic.
Each month, the Chapter Briefs Newsletter will feature an OU Analytics Tip that will be helpful to all who utilize the OU Analytics tool. This month's Tip features obtaining Membership Verification.
Each month, the Chapter Briefs Newsletter will feature an OU Analytics Tip that will be helpful to all who utilize the OU Analytics tool. This month's Tip features obtaining Membership Verification.
The IEEE Signal Processing Society (SPS) is launching PROGRESS - an initiative aiming to motivate and support women and under-represented minorities to pursue academic careers in signal processing. 2020 PROGRESS Workshop at ICIP 2020 (Virtual), 26-27 October 2020. Submission Deadline is 20 October 2020!
The IEEE Signal Processing Society (SPS) has extended the nomination period for the position of the Chair, Young Professionals Committee in order to allow for a more diverse slate of candidates.
Dr. Mari Ostendorf is an Endowed Professor of System Design Methodologies at the University of Washington in the Electrical & Computer Engineering Department, currently serving as Associate Vice Provost for Research. She is also an Adjunct Professor in Linguistics and in the Paul G. Allen School of Computer Science and Engineering.
Vimal Bhatia is a Professor in the Discipline of Electrical Engineering at the Indian Institute of Technology Indore, India. He is also an associated faculty with Centre for Advanced Electronics and Discipline of Astronomy, Astrophysics, and Space Engineering at IIT Indore.
Position description: The research project will focus on developing machine learning/deep learning methods for fundamental computer vision problems including object motion tracking, segmentation, 3D reconstruction, classification and image captioning in 2D/3D images including RGBD images, remote sensing data, 3D CT/MRI medical images and biomedical text.
October 26-27, 2020
Application submission deadline: October 14, 2020
Location: Virtual conference
Submission Deadline: October 12, 2020
Call for Proposals Document
K.V.S. Hari
SPS Vice President-Membership
Deadline: 16 October 2020
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning (ML) applications. It is used for solving optimization problems similarly to gradient-based methods. However, it does not require the gradient, using only function evaluations. Specifically, ZO optimization iteratively performs three major steps: gradient estimation, descent direction computation, and the solution update. In this article, we provide a comprehensive review of ZO optimization, with an emphasis on showing the underlying intuition, optimization principles, and recent advances in convergence analysis.
Optimization lies at the heart of machine learning (ML) and signal processing (SP). Contemporary approaches based on the stochastic gradient (SG) method are nonadaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application. This article summarizes recent research and motivates future work on adaptive stochastic optimization methods, which have the potential to offer significant computational savings when training largescale systems.
Many contemporary applications in signal processing and machine learning give rise to structured nonconvex nonsmooth optimization problems that can often be tackled by simple iterative methods quite effectively. One of the keys to understanding such a phenomenon-and, in fact, a very difficult conundrum even for experts-lies in the study of "stationary points" of the problem in question. Unlike smooth optimization, for which the definition of a stationary point is rather standard, there are myriad definitions of stationarity in nonsmooth optimization.
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