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2022 Chicago Workshop on Coding and Learning
December 2, 2022 @ 8:45 am - 4:15 pm CST
Source and channel coding have historically been the two fundamental tenets of information theory, respectively studying the ultimate performance limits to data compression and error correction. There have been many recent studies on the application of deep learning techniques to design or interpret new codes, and conversely, coding or information theoretic ideas have resulted in significant advances in various areas of machine learning. This workshop will bring together expert researchers who work in the intersection of coding and learning to present their latest contributions to the field, and also suggest future research directions. Specific topics of interest include, but not limited to:
– Interpretability/explainability in source and channel coding
– Deep learning aided coding schemes
– Coding for private and secure multi-agent learning
– Neural network compression, pruning, quantization
– Neural network capacity, approximation
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