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 1201–1225 of 1963 papers

TitleStatusHype
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
All your loss are belong to BayesCode0
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Learning Inconsistent Preferences with Gaussian Processes—0
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels—0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
Quadruply Stochastic Gaussian ProcessesCode1
Non-Euclidean Universal ApproximationCode0
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression EstimatorsCode0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Skew Gaussian Processes for ClassificationCode1
Longitudinal Deep Kernel Gaussian Process Regression—0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Global Optimization of Gaussian processes—0
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes—0
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesCode1
Stable spline identification of linear systems under missing data—0
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian ProcessesCode0
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization—0
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Planning from Images with Deep Latent Gaussian Process DynamicsCode0
Show:102550
← PrevPage 49 of 79Next →

Benchmark Results

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