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Improvements to the sequence memoizer

Abstract:

The sequence memoizer is a model for sequence data with state-of-the-art performance on language modeling and compression. We propose a number of improvements to the model and inference algorithm, including an enlarged range of hyperparameters, a memory-efficient representation, and inference algorithms operating on the new representation. Our derivations are based on precise definitions of the various processes that will also allow us to provide an elementary proof of the "mysterious" coagul...

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Journal:
Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Publication date:
2010-01-01
URN:
uuid:27294309-dd9e-4216-b49d-d809518b7617
Source identifiers:
353226
Local pid:
pubs:353226
Language:
English

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