Jacob Moss — accepted AI-conference papers

2023

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Yuanqi Du +22

NeurIPS 2023poster

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to molecular repr…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗

2021

Neural ODE Processes

Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

ICLR 2021poster

Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-p…

Cited by 84 Source ↗PDF ↗code ↗Scholar ↗