Search Results for author:
Found 5 papers, 1 papers with code
Split Hierarchical Variational Compression
Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids -- for the first time -- many of the practicalities commonly associated with bits-back coding.
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
In this paper, we discuss lossless compression using normalizing flows which have demonstrated a great capacity for achieving high compression ratios.
Ranked #1 on
Image Compression
on ImageNet32
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.
Black-Box Inference for Non-Linear Latent Force Models
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process.
Black-Box Autoregressive Density Estimation for State-Space Models
State-space models (SSMs) provide a flexible framework for modelling time-series data.
