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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 1963 papers

TitleStatusHype
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes—0
Fast Adaptation with Linearized Neural Networks—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Fast Adaptive Weight Noise—0
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering—0
Emulating dynamic non-linear simulators using Gaussian processes—0
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)—0
Meta-models for transfer learning in source localisation—0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Bézier Curve Gaussian Processes—0
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization—0
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting—0
Enhancing RSS-Based Visible Light Positioning by Optimal Calibrating the LED Tilt and Gain—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes—0
Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
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Benchmark Results

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