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 451–475 of 1963 papers

TitleStatusHype
Fast Approximate Multi-output Gaussian ProcessesCode0
Approximate Latent Force Model InferenceCode0
Multi-resolution Multi-task Gaussian ProcessesCode0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Deep Gaussian Processes with Importance-Weighted Variational InferenceCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Federated Causal Inference from Observational DataCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
Explainable Learning with Gaussian ProcessesCode0
Black-box Coreset Variational InferenceCode0
A Bayesian Take on Gaussian Process NetworksCode0
Exact Gaussian Processes on a Million Data PointsCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Deep learning with differential Gaussian process flowsCode0
Nonlinear Inverse Reinforcement Learning with Gaussian ProcessesCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Deep Multi-fidelity Gaussian ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Deep Neural Networks as Gaussian ProcessesCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
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Benchmark Results

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