Intersection of Information Theory and Signal Processing: New Signal Models, their Information Content and Acquisition Complexity (18w5162)

Arriving in Banff, Alberta Sunday, October 28 and departing Friday November 2, 2018

Organizers

(Mitsubishi Electric Research Laboratories)

(University of Toronto)

(Israel Institute of Technology (Technion))

Description

The Banff International Research Station will host the "Intersection of Information Theory and Signal Processing: New Signal Models, their Information Content and Acquisition Complexity" workshop from October 28th to November 2nd, 2018.


The Banff International Research Station will host the "Intersection of Information Theory and Signal Processing: New signal models, their information content and acquisition complexity'' workshop from in 2018.

Recent advances in signal processing and signal representation have brought to the forefront the need for a new understanding of the information content of signals and signal models. Developments such as sparsity and compressive sensing have led to great innovations in signal processing theory and practice. It is now widely appreciated that appropriate signal modeling can significantly reduce the acquisition burden and improve processing performance. However, there remains a lack of understanding of the information content of the new signal models, as well as the interaction between information content and signal acquisition complexity.

Simultaneously, recent advances in in information theory have demonstrated its applicability to a much wider range of fields, beyond its traditional applications in communications. Recent work, for example, has demonstrated information theoretical limits in classification performance and sparse signal recovery. Further, information theoretic perspectives have motivated new types of dimensionality reduction and clustering techniques, while the algorithm approaches prevalent in error correction have led to state-of-the-art sparse signal recovery techniques. However, despite these successes and great potential, the application of information theoretic tools in these other areas, such as machine learning and signal processing, is nascent.

The goal of this workshop is to bring together the signal processing, machine learning and information theory communities, foster interaction and collaboration across the fields, and promote cross-pollination of ideas. In light of initial efforts and results, we believe the time is ripe to spearhead this interaction. To this end, we are inviting researchers from all three areas who have demonstrated the interest and ability to cross the boundaries of their immediate field and venture into the others.



The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).