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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 1191–1200 of 1963 papers

TitleStatusHype
Continuous Optimization Benchmarks by SimulationCode0
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling—0
Multi-Agent Safe Planning with Gaussian Processes—0
Deterministic error bounds for kernel-based learning techniques under bounded noiseCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes—0
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard AssessmentCode0
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Benchmark Results

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