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2 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 ...
2 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 ...
Deep Kernel learning for reaction outcome prediction and optimization
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6 days ago · Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the ...
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) ...
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 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 ...
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 ...
1 day 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 ...
May 30, 2024 · Applying Bayesian Optimization (BO) BO is a strategy based on probabilistic models, typically employing a Gaussian process to predict the performance ...
Jun 6, 2024 · Gaussian processes are a versatile probabilistic machine learning model whose effectiveness often depends on good hyperparameters, which are typically ...