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 1461–1470 of 1963 papers

TitleStatusHype
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
Bayesian Anomaly Detection and Classification—0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Show:102550
← PrevPage 147 of 197Next →

Benchmark Results

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