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Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks


Mar 16, 2022
Gregory Kell, Ryan-Rhys Griffiths, Anthony Bourached, David G. Stork

* Accepted at Computer Vision and Image Analysis of Art (CVAA) conference 2022 

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Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion


Nov 29, 2021
Anthony Bourached, Robert Gray, Ryan-Rhys Griffiths, Ashwani Jha, Parashkev Nachev

* Under Review 

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Data Considerations in Graph Representation Learning for Supply Chain Networks


Jul 22, 2021
Ajmal Aziz, Edward Elson Kosasih, Ryan-Rhys Griffiths, Alexandra Brintrup

* ICML 2021 Workshop on Machine Learning for Data 

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High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning


Jun 16, 2021
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar


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Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design


May 06, 2021
Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee

* Presented at the 2018 NeurIPS Workshop on Machine Learning for Molecules and Materials 

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Computational identification of significant actors in paintings through symbols and attributes


Feb 04, 2021
David G. Stork, Anthony Bourached, George H. Cann, Ryan-Rhys Griffiths

* Accepted as conference paper at Computer Vision and Art Analysis 2021 

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Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks


Jan 30, 2021
George Cann, Anthony Bourached, Ryan-Rhys Griffiths, David Stork

* Accepted for Publication at Computer Vision and Art Analysis, IS&T, Springfield, VA, 2021 

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Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars


Jan 04, 2021
Anthony Bourached, George Cann, Ryan-Rhys Griffiths, David G. Stork

* Accepted to Electronic Imaging 2021: Computer Vision and image Analysis of Art 

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Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?


Dec 17, 2020
Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar


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Gaussian Process Molecule Property Prediction with FlowMO


Oct 14, 2020
Henry B. Moss, Ryan-Rhys Griffiths


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Generative Model-Enhanced Human Motion Prediction


Oct 05, 2020
Anthony Bourached, Ryan-Rhys Griffiths, Robert Gray, Ashwani Jha, Parashkev Nachev

* 8 pages + 5 pages supplementary materials, under review at ICLR 

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Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation


Oct 17, 2019
Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A. Aldrick, Alpha A. Lee

* Accepted to the 2019 NeurIPS Workshop on Safety and Robustness in Decision Making 

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Adaptive Sensor Placement for Continuous Spaces


May 16, 2019
James A Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S Leslie, Sattar Vakili, Enrique Munoz de Cote

* 13 pages, accepted to ICML 2019 

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Constrained Bayesian Optimization for Automatic Chemical Design


Jun 27, 2018
Ryan-Rhys Griffiths, José Miguel Hernández-Lobato

* Previous versions accepted to the NIPS 2017 Workshop on Bayesian Optimization (BayesOpt 2017) and the NIPS 2017 Workshop on Machine Learning for Molecules and Materials 

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