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 341–350 of 1963 papers

TitleStatusHype
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Compositionally-Warped Gaussian Processes—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs—0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
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Benchmark Results

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