We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively ...

This repository contains a Python implementation of the Wasserstein Distance, Wasserstein Barycenter and Optimal Transport Map of Gaussian Processes. Based on the papers: Mallasto, Anton, and Aasa ...

Python hacks to automate tasks, clean data, and perform advanced analytics in Excel. Boost productivity effortlessly in day ...

This is a potentially valuable modeling study on sequence generation in the hippocampus in a variety of behavioral contexts. While the scope of the model is ambitious, its presentation is incomplete ...

This important study investigates frequency-dependent effects of transcutaneous tibial nerve stimulation (TTNS) on bladder function in healthy humans and, through a computational model, shows that low ...

Abstract: Gaussian processes regression models are an appealing machine learning method as they learn expressive nonlinear models from exemplar data with minimal parameter tuning and estimate both the ...

To address these issues, we propose MetaGP, a meta-learning-based Gaussian process latent variable model that uses a Gaussian process kernel function to capture long-term dependencies and to maintain ...

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