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Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels
Our results show that there is an interesting interplay between source distortion (i. e., distortion for the probability vector measured via f-divergence) and the subsequent channel encoding/decoding parameters; and indicate that a joint design of these parameters is crucial to navigate the latency-distortion tradeoff.
Generalization Bounds for Neural Belief Propagation Decoders
We present new theoretical results which bound this gap and show the dependence on the decoder complexity, in terms of code parameters (blocklength, message length, variable/check node degrees), decoding iterations, and the training dataset size.
Unsupervised Change Detection using DRE-CUSUM
We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point.
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