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In this paper, depending on different tasks (dimension reduction and similarity metric learning), GP-Metric involves two main steps to predict the new test ...
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The goal of DML is to learn an application-specific distance metric that brings “similar” objects close together while separating “dissimilar” objects [11].
Abstract. Learning appropriate distance metric from data can significantly improve the performance of machine learning tasks under investigation.
... The Gaussian process (GP) model is a statistical model that defines a distribution over functions, embodying a collection of random variables where any ...
This paper presents Gaussian process meta-learning (GPML) for few-shot regression, which explicitly exploits the distance between regression problems/tasks ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
May 3, 2020 · Hello,. I'm trying to implement GP Regression in Python. I'm trying to estimate the error between the robotic arm and the position of the ...
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Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems.
Sep 2, 2020 · This article proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of ...
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Gaussian processes with deep neural networks demonstrate to be a strong learner for few-shot learning since they combine the strength of deep learning and ...
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