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 921–930 of 1963 papers

TitleStatusHype
The Fast Kernel TransformCode0
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization—0
Learning particle swarming models from data with Gaussian processes—0
Granger Causality from Quantized Measurements—0
Gaussian Processes on Hypergraphs—0
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes—0
JUMBO: Scalable Multi-task Bayesian Optimization using Offline DataCode0
A Markov Reward Process-Based Approach to Spatial InterpolationCode0
Gaussian Processes with Differential Privacy—0
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models—0
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Benchmark Results

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