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Explores random binning in advanced information theory, focusing on assigning labels based on typicality and achieving negligible error rates in source coding.
Explores optimal errors in high-dimensional models, comparing algorithms and shedding light on the interplay between model architecture and performance.
Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.
Explores maximal correlation in information theory, mutual information properties, Renyi's measures, and mathematical foundations of information theory.
