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 941–950 of 1963 papers

TitleStatusHype
Gaussian Process for Trajectories—0
Bayesian neural network unit priors and generalized Weibull-tail property—0
Probabilistic Metamodels for an Efficient Characterization of Complex Driving ScenariosCode0
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian ProcessesCode0
On the Correspondence between Gaussian Processes and Geometric Harmonics—0
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice—0
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learningCode0
Deep banach space kernels—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
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Benchmark Results

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