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Approximate inference in graphical models for digital communications


Information theory guides research in communication engineering and settle its theoretical limits. Advances in machine learning generative modelling have proven useful in providing state-of-the-art results in channel coding, one of the disciplines in information theory. The objective of this Marie Curie Fellowship proposal is to explore to what extent new algorithms in approximate inference for generative models can be beneficial not only to channel coding, but also to other information theory issues. In the last couple of years, there have been significant advances in solving the approximate inference problem in graphical models with loops.

These algorithms improve the approximation function and divergence measure of belief propagation, the algorithm use d to address the channel-coding problem. Moreover, most communication problems (i.e. source coding, channel coding, equalisation, multiple access) can be expressed as inference problems in graphs with loops. Hence, the application of belief propagation extensions to information theory is a natural research direction that can benefit the description of communications systems.

In this Fellowship we will focus on applying these extensions to open problems in information theory. Furthermore, we expect to draw conclusions about the performance of these approximate inference algorithms and improve them to meet our goals. The multidisciplinary nature of this fellowship proposes advances in information theory and machine learning.

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