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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 511–520 of 1963 papers

TitleStatusHype
Collaborative Gaussian Processes for Preference Learning—0
COBRA -- COnfidence score Based on shape Regression Analysis for method-independent quality assessment of object pose estimation from single images—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
A flexible state space model for learning nonlinear dynamical systems—0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
A probabilistic Taylor expansion with Gaussian processes—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
A theory of representation learning gives a deep generalisation of kernel methods—0
Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection—0
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Benchmark Results

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