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Deep parametric predictive Gaussian processes for uncertainty estimation
Olu Oyebamiji
Computer Science
Research output
:
Contribution to conference (unpublished)
›
Paper
›
peer-review
Overview
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Dive into the research topics of 'Deep parametric predictive Gaussian processes for uncertainty estimation'. Together they form a unique fingerprint.
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Keyphrases
Uncertainty Estimation
100%
Gaussian Process
100%
Deep Gaussian Process
100%
Bayesian Methods
33%
Model Complexity
33%
Computational Cost
33%
Integrated Assessment
33%
High-dimensional Data
33%
Complex Data
33%
Predictive Uncertainty
33%
Uncertainty Quantification
33%
Large-dimensional Data
33%
Learning Representations
33%
Assessment Platform
33%
Input Variability
33%
Point Method
33%
Multitask Problem
33%
Shapley Additive Explanations
33%
Efficient Approximation
33%
Uncertainty Approximation
33%
Shallow Model
33%
GP Regression
33%
Posterior Variance
33%
Spatial Factor Model
33%
Inducing Points
33%
Stochastic Variational Inference
33%
Computer Science
Approximation (Algorithm)
100%
Computational Cost
100%
High Dimensional Data
100%
Dimensional Data
100%
Uncertainty Estimation
100%
SHapley Additive exPlanation
100%
Mathematics
Parametric
100%
Gaussian Process
100%
Dimensional Data
40%
Stochastics
20%
Variance
20%
Bayesian
20%
Computational Cost
20%
Complex Model
20%
Uncertainty Quantification
20%
Input Feature
20%
Engineering
Gaussians
100%
Dimensional Data
40%
Complex Model
20%
Computational Cost
20%
Point Method
20%
Input Feature
20%
Uncertainty Quantification
20%
Economics, Econometrics and Finance
Gaussian Process
100%
Bayesian
20%
Factor Model
20%
Earth and Planetary Sciences
Uncertainty Modeling
100%