IEEE Signal Processing Magazine
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
Table of contents
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Table of Contents| 2020
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Masthead| 2020
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Revisiting Research on Signal Processing for Communications in a Pandemic [From the Editor]Heath, Robert W. et al. | 2020
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The Year of Living Dangerously [President's Message]Tewfik, Ahmed et al. | 2020
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Photo and Video Technologies Target New Frontiers: Innovative Imaging Research Enabled by Signal Processing Is Making Cameras More Powerful and Versatile [Special Reports]Edwards, John et al. | 2020
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Machine Learning From Distributed, Streaming Data [From the Guest Editors]Bajwa, Waheed U. / Cevher, Volkan / Papailiopoulos, Dimitris et al. | 2020
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Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine LearningNassif, Roula / Vlaski, Stefan / Richard, Cedric et al. | 2020
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Distributed Learning in the Nonconvex World: From batch data to streaming and beyondChang, Tsung-Hui / Hong, Mingyi / Wai, Hoi-To et al. | 2020
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Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategiesCui, Xiaodong / Zhang, Wei / Finkler, Ulrich et al. | 2020
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Federated Learning: Challenges, Methods, and Future DirectionsLi, Tian / Sahu, Anit Kumar / Talwalkar, Ameet et al. | 2020
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Optimally Compressed Nonparametric Online Learning: Tradeoffs between memory and consistencyKoppel, Alec / Bedi, Amrit Singh / Rajawat, Ketan et al. | 2020
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Optimization and Learning With Information Streams: Time-varying algorithms and applicationsDall'Anese, Emiliano / Simonetto, Andrea / Becker, Stephen et al. | 2020
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Distributed No-Regret Learning in Multiagent Systems: Challenges and Recent DevelopmentsXu, Xiao / Zhao, Qing et al. | 2020
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Distributed Gradient Methods for Convex Machine Learning Problems in Networks: Distributed OptimizationNedic, Angelia et al. | 2020
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Decentralized Stochastic Optimization and Machine Learning: A Unified Variance-Reduction Framework for Robust Performance and Fast ConvergenceXin, Ran / Kar, Soummya / Khan, Usman A. et al. | 2020
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Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient DescentPu, Shi / Olshevsky, Alex / Paschalidis, Ioannis Ch. et al. | 2020
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Optimization for Reinforcement Learning: From a single agent to cooperative agentsLee, Donghwan / He, Niao / Kamalaruban, Parameswaran et al. | 2020
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Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data ProcessingRamamoorthy, Aditya / Das, Anindya Bijoy / Tang, Li et al. | 2020
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Adversary-Resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat ModelYang, Zhixiong / Gang, Arpita / Bajwa, Waheed U. et al. | 2020
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An Overview of the MPEG-5 Essential Video Coding Standard [Standards in a Nutshell]Choi, Kiho / Chen, Jianle / Rusanovskyy, Dmytro et al. | 2020
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Privacy-Aware Human Activity Recognition From a Wearable Camera: Highlights From the IEEE Video And Image Processing Cup 2019 Student Competition [SP Competitions]Tadesse, Girmaw Abebe / Bent, Oliver / Marcenaro, Lucio et al. | 2020
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[Dates Ahead]| 2020
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The Information Forensics and Security Technical Committee: Then, Now, and in the Future [In the Spotlight]Rocha, Anderson de Rezende et al. | 2020
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Front Cover| 2020