scholar.google.com › citations
We use Gaussian Process (GP) to extend the bilinear similarity into a non-parametric metric (here we abuse the concept of metric) and then learn this metric ...
Learning appropriate distance metric from data can significantly improve the performance of machine learning tasks under investigation.
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 ...
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 ...
People also ask
What are Gaussian processes used for in machine learning?
What is the metric learning approach?
What is the Gaussian model process?
What is the Gaussian process regression in statistics?
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems ... metric to ...
Kernel methods, such as Gaussian processes, have had an exceptionally consequential impact on machine learning theory ... metric learning, deep learning ...
... Gaussian Processes for Machine Learning, C. E. Rasmussen ... High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning.
Jan 13, 2021 · Gaussian Process Manifold Learning is a novel model based machine learning method ... metric tensor. Geodesics solved using these new adaptations ...
Gaussian Processes for Machine Learning presents one of the most important. Bayesian machine learning approaches ... metric positive semidefinite, its eigenvalues ...