SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1131–1140 of 1963 papers

TitleStatusHype
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children—0
Contraction L_1-Adaptive Control using Gaussian Processes—0
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion MethodCode0
Information Theoretic Meta Learning with Gaussian Processes—0
Modulating Scalable Gaussian Processes for Expressive Statistical LearningCode0
Locally induced Gaussian processes for large-scale simulation experiments—0
Fast Approximate Multi-output Gaussian ProcessesCode0
Neural Networks and Quantum Field TheoryCode1
Preferential Bayesian optimisation with Skew Gaussian Processes—0
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified