Ryan-Rhys Griffiths | DeepAI

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∙ 03/24/2023

Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes

In many areas of the observational and experimental sciences data is sca...

0 Ryan-Rhys Griffiths, et al. ∙

∙ 01/31/2023

Mathematical Capabilities of ChatGPT

We investigate the mathematical capabilities of ChatGPT by testing it on...

0 Simon Frieder, et al. ∙

∙ 03/14/2022

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

We present a novel bi-modal system based on deep networks to address the...

13 Gregory Kell, et al. ∙

∙ 11/24/2021

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

Models of human motion commonly focus either on trajectory prediction or...

0 Anthony Bourached, et al. ∙

∙ 07/22/2021

Data Considerations in Graph Representation Learning for Supply Chain Networks

Supply chain network data is a valuable asset for businesses wishing to ...

0 Ajmal Aziz, et al. ∙

∙ 06/07/2021

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

We introduce a method based on deep metric learning to perform Bayesian ...

37 Antoine Grosnit, et al. ∙

∙ 05/06/2021

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

Datasets in the Natural Sciences are often curated with the goal of aidi...

17 Ryan-Rhys Griffiths, et al. ∙

∙ 02/04/2021

Computational identification of significant actors in paintings through symbols and attributes

The automatic analysis of fine art paintings presents a number of novel ...

10 David G. Stork, et al. ∙

∙ 01/30/2021

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

We apply generative adversarial convolutional neural networks to the pro...

3 George Cann, et al. ∙

∙ 12/15/2020

Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?

Bayesian optimisation presents a sample-efficient methodology for global...

15 Antoine Grosnit, et al. ∙

∙ 10/02/2020

Gaussian Process Molecule Property Prediction with FlowMO

We present FlowMO: an open-source Python library for molecular property ...

15 Henry B. Moss, et al. ∙

∙ 10/17/2019

Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation

Bayesian optimisation is an important decision-making tool for high-stak...

0 Ryan-Rhys Griffiths, et al. ∙

∙ 05/16/2019

Adaptive Sensor Placement for Continuous Spaces

We consider the problem of adaptively placing sensors along an interval ...

0 James A. Grant, et al. ∙

∙ 09/16/2017

Constrained Bayesian Optimization for Automatic Chemical Design

Automatic Chemical Design leverages recent advances in deep generative m...

0 Ryan-Rhys Griffiths, et al. ∙

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