GAUCHE: A Library for Gaussian Processes in Chemistry

1 code implementation • NeurIPS 2023 • Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Samuel Stanton, Gary Tom, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Durholt, Saudamini Chaurasia, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.

Bayesian Optimisation Gaussian Processes +1

Neural ODE Processes: A Short Summary

1 code implementation • NeurIPS Workshop DLDE 2021 • Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio

To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

Modular Neural Ordinary Differential Equations

no code implementations • 15 Sep 2021 • Max Zhu, Pietro Lio, Jacob Moss

The laws of physics have been written in the language of dif-ferential equations for centuries.

Meta-learning using privileged information for dynamics

1 code implementation • ICLR Workshop Learning_to_Learn 2021 • Ben Day, Alexander Norcliffe, Jacob Moss, Pietro Liò

Neural ODE Processes approach the problem of meta-learning for dynamics using a latent variable model, which permits a flexible aggregation of contextual information.

Meta-Learning

Neural ODE Processes

2 code implementations • ICLR 2021 • Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò

To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

Gene Regulatory Network Inference with Latent Force Models

no code implementations • 6 Oct 2020 • Jacob Moss, Pietro Lió

Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data.

Gaussian Processes Time Series +2