302 citations · 518 across the 20 of their papers we have counts for
8 papers · 1 filter
Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
Anthony Bourached, Robert Gray, Xiaodong Guan +3
Models of human motion commonly focus either on trajectory prediction or action classification but rarely both. The marked heterogeneity and intricate compositionality of human mot…
Data Considerations in Graph Representation Learning for Supply Chain Networks
Ajmal Aziz, Edward Elson Kosasih, Ryan-Rhys Griffiths +1
Supply chain network data is a valuable asset for businesses wishing to understand their ethical profile, security of supply, and efficiency. Possession of a dataset alone however…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee
Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machin…
Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes
Ryan-Rhys Griffiths, Jiachen Jiang, Douglas J. K. Buisson +12
The optical and UV variability of the majority of AGN may be related to the reprocessing of rapidly-changing X-ray emission from a more compact region near the central black hole.…
Computational identification of significant actors in paintings through symbols and attributes
David G. Stork, Anthony Bourached, George H. Cann +1
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representa…