Ryan-Rhys Griffiths
Ryan-Rhys Griffiths
Dirección de correo verificada de - Página principal
Citado por
Citado por
Mathematical Capabilities of ChatGPT
S Frieder, L Pinchetti, RR Griffiths, T Salvatori, T Lukasiewicz, P Petersen, ...
Advances in Neural Information Processing Systems 36, 2024
Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders
RR Griffiths, JM Hernández-Lobato
Chemical Science 11 (2), 577-586, 2020
HEBO: Pushing the Limits of Sample-Efficient Hyper-Parameter Optimisation
AI Cowen-Rivers, W Lyu, R Tutunov, Z Wang, A Grosnit, RR Griffiths, ...
Journal of Artificial Intelligence Research 74, 1269-1349, 2022
Mapping Materials and Molecules
B Cheng, RR Griffiths, S Wengert, C Kunkel, T Stenczel, B Zhu, ...
Accounts of Chemical Research 53 (9), 1981-1991, 2020
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
RR Griffiths*, A Grosnit*, R Tutunov*, AM Maraval*, AI Cowen-Rivers, ...
arXiv preprint arXiv:2106.03609, 2021
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes
RR Griffiths, JL Greenfield, AR Thawani, AR Jamasb, HB Moss, ...
Chemical Science 13, 13541 - 13551, 2022
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
RR Griffiths, AA Aldrick, M Garcia-Ortegon, V Lalchand
Machine Learning: Science and Technology 3 (1), 015004, 2021
GAUCHE: A Library for Gaussian Processes in Chemistry
RR Griffiths, L Klarner, H Moss, A Ravuri, S Truong, Y Du, S Stanton, ...
Advances in Neural Information Processing Systems 36, 2024
Gaussian Process Molecule Property Prediction with FlowMO
HB Moss*, RR Griffiths*
NeurIPS 2020: Workshop on ML4Molecules, 2020
Modeling the Multiwavelength Variability of Mrk 335 Using Gaussian Processes
RR Griffiths, J Jiang, DJK Buisson, D Wilkins, LC Gallo, A Ingram, ...
The Astrophysical Journal 914 (2), 144, 2021
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
RR Griffiths, P Schwaller, AA Lee
arXiv preprint arXiv:2105.02637, 2021
Adaptive Sensor Placement for Continuous Spaces
JA Grant, A Boukouvalas, RR Griffiths, DS Leslie, S Vakili, EM De Cote
ICML 2019, 2019
Recovery of Underdrawings and Ghost-Paintings via Style Transfer by Deep Convolutional Neural Networks: A Digital Tool for Art Scholars
A Bourached, G Cann, RR Griffiths, DG Stork
Electronic Imaging 2021, 2021
Generative Model‐Enhanced Human Motion Prediction
A Bourached, RR Griffiths, R Gray, A Jha, P Nachev
Applied AI Letters 3 (2), e63, 2022
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation?
A Grosnit, AI Cowen-Rivers, R Tutunov, RR Griffiths, J Wang, ...
Journal of Machine Learning Research, 22(160), 1-78., 2021
Data Considerations in Graph Representation Learning for Supply Chain Networks
A Aziz, EE Kosasih, RR Griffiths, A Brintrup
ICML 2021: Workshop on ML4Data, 2021
Bayesian Optimisation for Additive Screening and Yield Improvements–Beyond One-Hot Encoding
B Ranković, RR Griffiths, HB Moss, P Schwaller
Digital Discovery, 2024
Computational Identification of Significant Actors in Paintings through Symbols and Attributes
DG Stork, A Bourached, GH Cann, RR Griffiths
Electronic Imaging 2021, 2021
High-Dimensional Bayesian Optimization with Invariance
E Verma, S Chakraborty, RR Griffiths
ICML Workshop on Adaptive Experimental Design and Active Learning, 2022
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
RR Griffiths
University of Cambridge, 2023
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20