4 citations · 8 across the 3 of their papers we have counts for
5 papers
Program Analysis of Probabilistic Programs
Maria I. Gorinova
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practi…
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an…
Automatic Reparameterisation of Probabilistic Programs
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow mode…
Effect Handling for Composable Program Transformations in Edward2
Dave Moore, Maria I. Gorinova
Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects. They have found sever…
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifice…