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 1471–1480 of 1963 papers

TitleStatusHype
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test EvaluationsCode0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
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Benchmark Results

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