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 1401–1410 of 1963 papers

TitleStatusHype
Deep Compositional Spatial Models—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Reliable training and estimation of variance networksCode0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Streaming Variational Monte CarloCode0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Bayesian Deconditional Kernel Mean Embeddings—0
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Benchmark Results

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