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 691–700 of 1963 papers

TitleStatusHype
Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps—0
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification—0
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing—0
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling—0
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates—0
Distributed Gaussian Process Based Cooperative Visual Pursuit Control for Drone Networks—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data—0
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoodsCode0
Show:102550
← PrevPage 70 of 197Next →

Benchmark Results

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