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Found 11 papers, 6 papers with code
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.
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.
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).
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.
Unsupervised Videographic Analysis of Rodent Behaviour
Animal behaviour is complex and the amount of data in the form of video, if extracted, is copious.
Raiders of the Lost Art
Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image.
