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 1281–1290 of 1963 papers

TitleStatusHype
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes—0
Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty—0
Wilsonian Renormalization of Neural Network Gaussian Processes—0
Bayesian Optimization using Deep Gaussian Processes—0
Wrapped Gaussian Process Regression on Riemannian Manifolds—0
Bayesian Deconditional Kernel Mean Embeddings—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
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Benchmark Results

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