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 801–810 of 1963 papers

TitleStatusHype
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Learning-based attacks in cyber-physical systems—0
A Hybrid Approach for Trajectory Control Design—0
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes—0
Learning particle swarming models from data with Gaussian processes—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
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Benchmark Results

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