propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP). •. use GP to extend the bilinear similarity into a non ...

To the best of our knowledge, this is the first work that directly uses GP as non-parametric metric. In the experiments, we compare our approach with related GP ...

Gaussian process approach for metric learning ... Abstract. Learning appropriate distance metric from data can significantly improve the performance of machine ...

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 ...

In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.

Ability of Gaussian process... · Gaussian Processes · GaussianProcessRegressor

This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative ...

Gaussian process approach for metric learning. Highlights•propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP).