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 261–270 of 1963 papers

TitleStatusHype
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Algorithmic Linearly Constrained Gaussian Processes—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
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Benchmark Results

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