Any model that is linear in its parameters with a Gaussian distribution over the parameters is a Gaussian process. This class spans discrete models, including ...
Missing: metric | Show results with:metric
Gaussian Processes for Machine Learning presents one of the most important. Bayesian machine learning approaches ... metric positive semidefinite, its ...
Jun 7, 2021 · By adapting ideas from deep metric learning, we use label guidance from the ... Gaussian process fit and yielding improved BO performance.
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian pro- cesses (GP). The GP-MIL model inherits two crucial benefits from GP: (i) ...
trasts sharply with the popular metric-based meta- learning approach which is based on the distance between data inputs or their embeddings in the few- shot ...
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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Aug 15, 2018 · The metric learning inference was based on multiclass Gaussian process classification. Using the popular datasets IAM and CVL combined, the ...
Reachability-based safe learning with Gaussian processes - IEEE Xplore
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We propose a novel method that uses a principled approach to learn the system's unknown dynamics based on a Gaussian process model and iteratively approximates ...
The efficiency and performance of the proposed real-time learning approach is demonstrated in ... Experts Model for Large-Scale Gaussian Process Regres- sion.
Unlike traditional paradigms where the useful knowledge from multiple source tasks is transferred through distance metric, our proposal firstly converts the ...