Fast Text Compression with Neural Networks

Matthew V. Mahoney, Florida Institute of Technology, USA

Neural networks have the potential to extend data com-pression algorithms beyond the character 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-Ziv compressors (zip, gzip, compress), and is competitive in time, space, and compression ratio with PPM and Burrows-Wheeler algorithms, currently the best known. The compressor, a bit-level predictive arithmetic encoder is fast because only 4-5 connections are simultaneously active and because it uses a variable learning rate optimized for one-pass training.


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