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 1201–1210 of 1963 papers

TitleStatusHype
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
All your loss are belong to BayesCode0
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Learning Inconsistent Preferences with Gaussian Processes—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
Quadruply Stochastic Gaussian ProcessesCode1
Non-Euclidean Universal ApproximationCode0
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression EstimatorsCode0
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Benchmark Results

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