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6 days ago · The Gaussian Process model is a nonparametric model widely used in machine learning to perform regression and prediction tasks, see [21] . Report issue for ...
6 days ago · We present a principled study on defining Gaussian processes (GPs) with inputs on the product of directional manifolds. A circular kernel is first presented ...
6 days ago · We introduce a simple and scalable method for training Gaussian process (GP) models that exploits cross-validation and nearest neighbor truncation. To ...
May 30, 2024 · The traditional approach uses supervised learning, in which an expert ... However, this metric calculates the distance as the average of all distances in ...
Deep Kernel learning for reaction outcome prediction and optimization
www.nature.com › ... › articles
Jun 14, 2024 · Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the ...
May 30, 2024 · A scheme that uses Gaussian processes to interpolate and marginalize over waveform error is adapted and investigated as a possible precursor solution to this ...
May 30, 2024 · The objective is to use a machine learning approach to find out optimal values of surface diffusivity and inter-particle distance for maximizing neck size for ...
May 29, 2024 · Gaussian processes are a versatile probabilistic machine learning model ... We apply these metrics to Gaussian Processes (GPs), Ensemble Deep Neural Nets (DNNs) ...
5 days ago · In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are ...
Treed Gaussian processes for animal movement modeling - PMC - NCBI
www.ncbi.nlm.nih.gov › PMC11144715
Jun 2, 2024 · We implemented a novel application of treed Gaussian process (TGP) models, a recently developed Bayesian machine learning technique, to animal movement modeling ...