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 1226–1250 of 1963 papers

TitleStatusHype
Gene Regulatory Network Inference with Latent Force Models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Global Optimization of Gaussian processes—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Global Optimization with Parametric Function Approximation—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
GP Kernels for Cross-Spectrum Analysis—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Granger Causality from Quantized Measurements—0
Graph Classification Gaussian Processes via Spectral Features—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Graph Convolutional Gaussian Processes—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
Graphical LASSO Based Model Selection for Time Series—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
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Benchmark Results

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