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 1401–1410 of 1963 papers

TitleStatusHype
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
A probabilistic data-driven model for planar pushing—0
A probabilistic Taylor expansion with Gaussian processes—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
Architectures and random properties of symplectic quantum circuits—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems—0
Artificial Neural Network and Deep Learning: Fundamentals and Theory—0
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs—0
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Benchmark Results

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