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 481–490 of 1963 papers

TitleStatusHype
Architectures and random properties of symplectic quantum circuits—0
Deep Random Splines for Point Process Intensity Estimation—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Sigma Point Processes—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
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Benchmark Results

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