scholar.google.com › citations
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.
Compared with the existing GP-based feature learning approaches, our approach can provide accurate similarity prediction in the new feature space. To the best ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
People also ask
What are Gaussian processes used for in machine learning?
What is the metric learning approach?
When to use Gaussian process model?
What is the theory of Gaussian processes?
We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all ...
This paper presents Gaussian process meta-learning (GPML) for few-shot ... It contrasts sharply with the popular metric-based meta-learning approach ...
These methods are under the metric- based meta-learning approach which aims to enforce similar images (or their embeddings) to belong to the same class. The ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
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. ...
In the priors notebook, we focus on how to specify Gaussian process priors. We directly connect the tradiational weight-space approach to modelling to ...
Missing: metric | Show results with:metric