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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 951–960 of 1963 papers

TitleStatusHype
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Input Dependent Sparse Gaussian Processes—0
Input Warping for Bayesian Optimization of Non-stationary Functions—0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Bayesian Optimization with Tree-structured Dependencies—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Inter-domain Deep Gaussian Processes—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
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Benchmark Results

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