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 541–550 of 1963 papers

TitleStatusHype
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Entropy of Overcomplete Kernel Dictionaries—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models—0
Building 3D Generative Models from Minimal Data—0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
Adversarially Robust Optimization with Gaussian Processes—0
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Benchmark Results

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