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GP, as a Bayesian non-parametric approach, has been widely used in many machine learning scenarios. Compared with other machine learning methods, GP provides a ...
As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
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We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
This paper presents Gaussian process meta-learning (GPML) for few-shot ... It contrasts sharply with the popular metric-based meta-learning approach ...
It allows to give a probabilistic approach to prediction by giving the mean and standard deviation as output when predicting. ... Rasmussen and Christopher K.I. ...
This approach to prediction uses a Gaussian process, a stochastic process that induces a Gaussian distribution on y based on the values of x. This approach.
Jan 16, 2023 · This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics ...
GPML belongs to both the gradient-based meta-learning approach, which searches for an initializer for a new task, and the metric- based meta-learning approach ...