… Models are best formed adaptively, based on the text seen so far. This paper surveys
successful strategies for adaptive modeling that are suitable for use in practical text compression …

… of distributed training of the models. Fortunately, there are a … communities on compression
of large deep learning models. In … organization of model compression methods for text. In this …

… We wish to use language models developed for text compression as the basis of a text …
The motivation for using these models is that they are weH grounded in information theory, and …

… Then, given a test document (different from the training documents), we compress it according
to each model and calculate the gain in per-symbol compression obtained by using M a …

… A fundamental problem in both NLP and text compression is modeling: the ability to
distinguish between high probability strings like recognize speech and low probability strings like …

… level n-gram models now in use, but have usually been avoided because they are too slow
to be practical. We introduce a model that produces better compression than popular Limpel-…

… taken to the problem in adaptive text compression. Although several methods have been …
a well-founded model. We propose the application of a Poisson process model of novelty. Its …

… compress the function that is learned by a complex model into a much smaller, faster model
… The key difficulty when compressing complex ensembles into simpler models this way is the …

… As an alternative, we wish to adopt language models based on well performing text compression
techniques such as PPM which have already been found to be highly effective in many …

… -l model did not provide satisfactory compression performance and the order-2-1-and-0
model produces compression … The order-2-and-0 model allows faster encoding and decoding …