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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 551–560 of 1963 papers

TitleStatusHype
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
On the role of Model Uncertainties in Bayesian Optimization—0
Modeling the evolution of temporal knowledge graphs with uncertainty—0
Machine learning methods for prediction of breakthrough curves in reactive porous media—0
Application of machine learning to gas flaring—0
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Benchmark Results

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