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 926–950 of 1963 papers

TitleStatusHype
Gaussian Process on the Product of Directional Manifolds—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Gaussian Process Neurons—0
Gaussian Process Morphable Models—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
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Benchmark Results

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