Lossless compression with state space models using bits back coding

03/18/2021
by   James Townsend, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/29/2019

Stochastic Gradient MCMC for Nonlinear State Space Models

State space models (SSMs) provide a flexible framework for modeling comp...
research
06/10/2021

Unsupervised Neural Hidden Markov Models with a Continuous latent state space

We introduce a new procedure to neuralize unsupervised Hidden Markov Mod...
research
12/01/2018

Explainable Genetic Inheritance Pattern Prediction

Diagnosing an inherited disease often requires identifying the pattern o...
research
04/29/2015

Market forecasting using Hidden Markov Models

Working on the daily closing prices and logreturns, in this paper we dea...
research
05/03/2021

Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models

To understand the long-run behavior of Markov population models, the com...
research
02/22/2021

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Latent variable models have been successfully applied in lossless compre...
research
12/20/2019

HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models

We make the following striking observation: fully convolutional VAE mode...

Please sign up or login with your details

Forgot password? Click here to reset