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 851–875 of 1963 papers

TitleStatusHype
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Group Importance Sampling for Particle Filtering and MCMC—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Harmonizable mixture kernels with variational Fourier features—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Gauss-Legendre Features for Gaussian Process Regression—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Gaussian Process Volatility Model—0
Current Methods for Drug Property Prediction in the Real World—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
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Benchmark Results

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