May 28, 2018 · In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
This paper studies the problem of deriving fast and accurate classification algo- rithms with uncertainty quantification. Gaussian process classification ...
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. In proceedings of NeurIPS, pages 6008-6018, 2018.
This paper is concerned with multi-class classification using GPs, placing particular emphasis on producing well calibrated probability estimates.
This paper studies the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification ...
A novel approach based on interpreting the labels as the output of a Dirichlet distribution is proposed that provides essentially the same accuracy and ...
May 28, 2018 · In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian ...
We follow the method of Dirichlet-based Gaussian Processes for Large-Scale Calibrated Classification who transform classification targets into regression ones ...
May 28, 2018 · In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian ...
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