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IEEE Journal on Selected Areas in Information Theory is a peer-reviewed journal published by IEEE, covering Wireless Communication Security Techniques, Stochastic Gradient Optimization Techniques, Error Correcting Code Techniques.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Journal on Selected Areas in Information Theory, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Journal on Selected Areas in Information Theory style. No sign-up. |
| Publishes research in | Wireless Communication Security Techniques Stochastic Gradient Optimization Techniques Error Correcting Code Techniques Privacy-Preserving Technologies in Data Cryptography and Data Security |
| ISSN | 2641-8770 |
| Citation impact (2-yr) | 2.14 |
| h-index | 34 |
| i10-index | 116 |
| Total citations | 5,321 |
| Top institutions publishing here | Stanford University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along…
We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity-achieving channel coding, which ignores the feedback signal, achieves the optimal performance. It is well known that separation is not optimal in the…
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art…
Many modern neural network architectures are trained in an overparameterized regime where the parameters of the model exceed the size of the training dataset. Sufficiently overparameterized neural network architectures in principle have the capacity to fit any set of labels including random noise. However, given the highly nonconvex nature of the training landscape it is…
Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal processing, and information theory, and has a wide range of applications. This survey starts with the basics of sequential change detection, and then moves on to generalizations and extensions of…
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