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Found 3 papers, 1 papers with code
Fast and Sample-Efficient Domain Adaptation for Autoencoder-Based End-to-End Communication
In this paper, we address the setting where the target domain has only limited labeled data from a distribution that is expected to change frequently.
Few-Shot Domain Adaptation For End-to-End Communication
The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach.
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
We fill this crucial research gap by proposing a new lossless compression scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to MGD in a way that preserves tuple boundaries within mini-batches.
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