Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …

Learning generative models that span multiple data modalities, such as vision and
language, is often motivated by the desire to learn more useful, generalisable …

Many problems in the physical sciences, machine learning, and statistical inference
necessitate sampling from a high-dimensional, multimodal probability distribution. Markov …

Many real-world sequential decision making problems are partially observable by nature,
and the environment model is typically unknown. Consequently, there is great need for …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

We provide theoretical and empirical evidence that using tighter evidence lower bounds
(ELBOs) can be detrimental to the process of learning an inference network by reducing the …

When used as a surrogate objective for maximum likelihood estimation in latent variable
models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this …

Particle Filtering (PF) methods are an established class of procedures for performing
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …

Data assimilation is concerned with sequentially estimating a temporally evolving state. This
task, which arises in a wide range of scientific and engineering applications, is particularly …