Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels

arXiv:2404.14586v1 Announce Type: cross
Abstract: In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive applications, transmission of these decisions in a reliable and timely manner is of significant importance. To this end, we study the scenario where a probability vector (representing the decisions of a classifier) at the transmitter, needs to be transmitted over a noisy channel. Assuming that the distortion …

abstract applications arxiv channels classification classifier cs.it cs.lg cs.ni decisions importance latency machine machine learning math.it results type work