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 1501–1510 of 1963 papers

TitleStatusHype
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Building 3D Generative Models from Minimal Data—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Causal Inference using Gaussian Processes with Structured Latent Confounders—0
Chance Constrained Stochastic Optimal Control for Arbitrarily Disturbed LTI Systems Via the One-Sided Vysochanskij-Petunin Inequality—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
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Benchmark Results

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