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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 671–680 of 1963 papers

TitleStatusHype
Random forests for binary geospatial data—0
Sharp Calibrated Gaussian Processes—0
Improved uncertainty quantification for neural networks with Bayesian last layerCode0
Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Learning Choice Functions with Gaussian ProcessesCode0
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Benchmark Results

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