Information Theory Society
The IEEE Information Theory Society is the premier professional society dedicated to the advancement of the mathematical underpinnings of information technology for the benefit of humanity. Information theory encompasses the processing, transmission, storage, and use of information, and the foundations of the communication process.
Meet IT Society StudentsThe Student and Outreach Subcommittee plans student and outreach activities at symposia and workshops.
Read Our NewsletterThe IEEE Information Theory Society Newsletter connects our members and is published four times a year.
Technical University of Vienna (TU Wien), Austria
The 2021 Croucher Summer Course in Information Theory was held in August 2021 at The Chinese…
Please submit any contributions for the society newsletter by October 31st.
The 2022 International Zurich Seminar on Information and Communication will take place on March 2–4…
Several PhD and PostDoc Positions: 6G Communication Systems, Aerial & Satellite Communications, Swarm Exploration, and Quantum Communications
The Department of Communications Engineering at the University of Bremen, Germany, invites…
Postdoctoral position at University of Toronto in information theory and machine learning
Call to Action
Submit to IEEE Transactions on Information Theory
The IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information. Papers published in the IEEE Transactions on Information Theory should contain a strong conceptual or analytical contribution.
Recent Journal Issues
JSAIT is a multi-disciplinary journal of special issues.
Research In Information Theory
Using networks as a means of computing can reduce the communication flow over networks. We propose to distribute the computation load in stationary networks and formulate a flow-based delay minimization problem that jointly captures the costs of communications and computation. We exploit the distributed compression scheme of Slepian-Wolf that is applicable under any protocol information. We introduce the notion of entropic surjectivity as a measure of function’s sparsity and to understand the li...
Reed-Muller (RM) codes are among the oldest, simplest and perhaps most ubiquitous family of codes. They are used in many areas of coding theory in both electrical engineering and computer science. Yet, many of their important properties are still under investigation. This paper covers some of the recent developments regarding the weight enumerator and the capacity-achieving properties of RM codes, as well as some of the algorithmic developments. In particular, the paper discusses the recent conn...
This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the learning algorithm and on the amount of training data. Concretely, we consider Kolmogorov-optimal approximation through deep neural networks with the guiding theme being a relation between the complexity of the function (class) to be approximated and the complexity of the approximating network in terms of connectivity and memory requirements for storing t...
We generalize alternating optimization algorithms of Blahut-Arimoto type to the quantum setting. In particular, we give iterative algorithms to compute the mutual information of quantum channels, the thermodynamic capacity of quantum channels, the coherent information of less noisy quantum channels, and the Holevo quantity of classical-quantum channels. Our convergence analysis is based on quantum entropy inequalities and leads to a priori additive eps-approximations after O(eps^(-1)*log N) iter...
Many modern applications involve accessing and processing graphical data, i.e. data that is naturally indexed by graphs. Examples come from internet graphs, social networks, genomics and proteomics, and other sources. The typically large size of such data motivates seeking efficient ways for its compression and decompression. The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees. In this paper, we introduce a low-complexity lossless com...