Search Results for author:
Found 19 papers, 12 papers with code
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
GPs can make predictions with consideration of uncertainty, for example in the virtual screening of molecules and materials, and can also make inferences about incomplete data such as the latent emission signature from a black hole accretion disc.
Mathematical Capabilities of ChatGPT
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
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 problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine art paintings and drawings.
Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
Models of human motion commonly focus either on trajectory prediction or action classification but rarely both.
Data Considerations in Graph Representation Learning for Supply Chain Networks
It is anticipated that our method will be directly applicable to businesses wishing to sever links with nefarious entities and mitigate risk of supply failure.
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
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 aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning.
Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes
Such a reprocessing model would be characterised by lags between X-ray and optical/UV emission due to differences in light travel time.
Gaussian Processes
High Energy Astrophysical Phenomena
Computational identification of significant actors in paintings through symbols and attributes
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs.
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 problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution.
Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars
Past methods for inferring color in underdrawings have been based on physical x-ray fluorescence spectral imaging of pigments in ghost-paintings and are thus expensive, time consuming, and require equipment not available in most conservation studios.
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
Generative Model-Enhanced Human Motion Prediction
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD).
Gaussian Process Molecule Property Prediction with FlowMO
We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes.
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes
Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications.
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Constrained Bayesian Optimization for Automatic Chemical Design
Automatic Chemical Design is a framework for generating novel molecules with optimized properties.
