25 citations · 49 across the 6 of their papers we have counts for
6 papers
Split Hierarchical Variational Compression
Tom Ryder, Chen Zhang, Ning Kang +1
Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has pro…
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Shifeng Zhang, Ning Kang, Tom Ryder +1
It was estimated that the world produced () of data in 2020, resulting in the enormous costs of both data storage and transmission. Fortunately, rece…
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Tom Ryder, Dennis Prangle, Andrew Golightly +1
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most a…
Black-Box Inference for Non-Linear Latent Force Models
Wil O. C. Ward, Tom Ryder, Dennis Prangle +1
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 Gaussia…
Black-Box Autoregressive Density Estimation for State-Space Models
Tom Ryder, Andrew Golighty, A. Stephen McGough +1
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and ep…
Black-box Variational Inference for Stochastic Differential Equations
Thomas Ryder, Andrew Golightly, A. Stephen McGough +1
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the di…