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 1401–1425 of 1963 papers

TitleStatusHype
Modulating Surrogates for Bayesian Optimization—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
Sequential Neural ProcessesCode0
Compositionally-Warped Gaussian Processes—0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Black-Box Inference for Non-Linear Latent Force Models—0
Multi-resolution Multi-task Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Learning Directed Graphical Models from Gaussian Data—0
Recurrent Neural ProcessesCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Kernelized Capsule NetworksCode0
Deep Compositional Spatial Models—0
Approximate Inference Turns Deep Networks into Gaussian ProcessesCode0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Reliable training and estimation of variance networksCode0
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves—0
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control—0
Streaming Variational Monte CarloCode0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Bayesian Deconditional Kernel Mean Embeddings—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Show:102550
← PrevPage 57 of 79Next →

Benchmark Results

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