Papers › Lossless compression with state space models using bits back coding

Lossless compression with state space models using bits back coding

18 Mar 2021ICLR Workshop Neural_Compression 2021 5arXiv:2103.10150archive 2025-07-28

James Townsend, Iain Murray

We generalize the 'bits back with ANS' method to time-series models with a latent Markov structure. This family of models includes hidden Markov models (HMMs), linear Gaussian state space models (LGSSMs) and many more. We provide experimental evidence that our method is effective for small scale models, and discuss its applicability to larger scale settings such as video compression.

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exp2 j-towns/ssm-code/hmm_codec.py official repository ran · honoured contract fingerprinted MIT (permissive) · 84676b39f4066505 · report
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quantized_cdf j-towns/ssm-code/hmm_codec.py official repository ran · our draft was wrong MIT (permissive) · 26b008e9485f5975 · report

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State Space ModelsTime SeriesTime Series AnalysisVideo Compression

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