Analyzing Training Using Phase Transitions in Entropy---Part I: General Theory
Abstract
We analyze phase transitions in the conditional entropy of a sequence caused by a change in the conditional variables. Such transitions happen, for example, when training to learn the parameters of a system, since the transition from the training phase to the data phase causes a discontinuous jump in the conditional entropy of the measured system response. For large-scale systems, we present a method of computing a bound on the mutual information obtained with one-shot training, and show that this bound can be calculated using the difference between two derivatives of a conditional entropy. The system model does not require Gaussianity or linearity in the parameters, and does not require worst-case noise approximations or explicit estimation of any unknown parameters. The model applies to a broad range of algorithms and methods in communication, signal processing, and machine learning that employ training as part of their operation.
Cite
@article{arxiv.2012.00970,
title = {Analyzing Training Using Phase Transitions in Entropy---Part I: General Theory},
author = {Kang Gao and Bertrand Hochwald},
journal= {arXiv preprint arXiv:2012.00970},
year = {2021}
}