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 381–390 of 1963 papers

TitleStatusHype
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images—0
Hodge-Compositional Edge Gaussian ProcessesCode0
Deep Transformed Gaussian Processes—0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regressionCode0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
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Benchmark Results

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