Feb 18, 2020 · A new algorithm for compressing latent representations in deep probabilistic models, such as variational autoencoders, in post-processing, ...

Here, we propose a new algorithm for compressing latent representations in deep probabilistic models, such as variational autoencoders, in post-processing. The ...

Papertalk is an open-source platform where scientists share video presentations about their newest scientific results - and watch, like + discuss them.

Single-model compression at variable bitrates. The decoupling of modeling and compression allows us to adjust the trade-off between bitrate and distortion in ...

Feb 18, 2020 · We propose a novel algorithm for quantizing continuous latent representations in trained models. Our approach applies to deep probabilistic models.

My group performs research on scalable approximate Bayesian inference methods (especially variational methods) and on deep probabilistic models, with ...

Sep 4, 2024 · A Compact Representation for Bayesian Neural Networks By Removing ... Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding.

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding · Yibo YangRobert BamlerS. Mandt. Computer Science. International Conference on Machine ...

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding · pdf icon ... An Introduction to Neural Data Compression · pdf icon · hmtl icon · Yibo Yang ...

May 8, 2020 · Firstly, their models contain a stochastic en- coder which is not suitable for lossy compression, where bits-back coding is inapplicable.