Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model

By Brendan J. Frey, Geoffrey E. Hinton. 1997

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

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In Probabilistic & Bayesian Methods

1. Jonathan Ho
2. Pieter Abbeel
3. José Miguel Hernández-Lobato
4. Robert Peharz
5. Evan Lohn
6. Stefano Ermon
7. Sergey Levine
8. Yang Song

Citations in the corpus

• Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters (2018, background): “In (Hinton & Zemel, 1994; Frey & Hinton, 1997) coding schemes were proposed which practically exploited the bits-back argument for the purpose of coding data .”
• Compression with Flows via Local Bits-Back Coding (2019, methodology): “There is already a rich literature of tailored coding algorithms for autoregressive models and variational autoencoders built from conditional distributions which are already tractable for coding [38, 12, 18, 13, 42], but there are currently no such algorithms for general types of flow models [32].”