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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 1963 papers

TitleStatusHype
Local Granger Causality—0
High-Dimensional Bayesian Optimization via Nested Riemannian ManifoldsCode1
Probabilistic Numeric Convolutional Neural NetworksCode1
Statistical Analysis of the LMS Algorithm for Proper and Improper Gaussian Processes—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Semi-parametric γ-ray modeling with Gaussian processes and variational inferenceCode0
Probabilistic selection of inducing points in sparse Gaussian processesCode1
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural NetworksCode0
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspacesCode1
Graph Based Gaussian Processes on Restricted Domains—0
Control Barrier Functions for Unknown Nonlinear Systems using Gaussian Processes—0
Few-shot Learning for Spatial Regression—0
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning—0
Splitting Gaussian Process Regression for Streaming Data—0
Recyclable Gaussian ProcessesCode1
Gene Regulatory Network Inference with Latent Force Models—0
Detecting Misclassification Errors in Neural Networks with a Gaussian Process ModelCode0
Deep kernel processes—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Predicting the Outputs of Finite Networks Trained with Noisy Gradients—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Multi-task Causal Learning with Gaussian ProcessesCode1
Semi-Supervised Image Deraining using Gaussian ProcessesCode1
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Benchmark Results

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