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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 661–670 of 1963 papers

TitleStatusHype
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
Low-Precision Arithmetic for Fast Gaussian ProcessesCode1
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian ProcessesCode1
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Off-the-grid learning of mixtures from a continuous dictionary—0
On the Rényi Cross-Entropy—0
Supernova Light Curves Approximation based on Neural Network ModelsCode1
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
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Benchmark Results

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