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 1501–1525 of 1963 papers

TitleStatusHype
Evolving-Graph Gaussian ProcessesCode0
Entropic Trace Estimates for Log DeterminantsCode0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Exact Gaussian Processes on a Million Data PointsCode0
Linearly Constrained Neural NetworksCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Physics-Informed Variational State-Space Gaussian ProcessesCode0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Chained Gaussian ProcessesCode0
Deep Convolutional Networks as shallow Gaussian ProcessesCode0
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear TimeCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Explainable Learning with Gaussian ProcessesCode0
Planning from Images with Deep Latent Gaussian Process DynamicsCode0
All your loss are belong to BayesCode0
Variational Implicit ProcessesCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
EigenGP: Gaussian Process Models with Adaptive EigenfunctionsCode0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Calibrated Computation-Aware Gaussian ProcessesCode0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsCode0
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Benchmark Results

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