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 426–450 of 1963 papers

TitleStatusHype
Les Houches Lectures on Deep Learning at Large & Infinite Width—0
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space ModelsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Latent Variable Multi-output Gaussian Processes for Hierarchical DatasetsCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging—0
Federated Causal Inference from Observational DataCode0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities—0
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy—0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Current Methods for Drug Property Prediction in the Real World—0
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Amortized Variational Inference: When and Why?Code0
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes—0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations—0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Efficient Determination of Safety Requirements for Perception Systems—0
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Benchmark Results

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