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 1361–1370 of 1963 papers

TitleStatusHype
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Amortized variance reduction for doubly stochastic objectives—0
Amortized Variational Inference for Deep Gaussian Processes—0
Analogical-based Bayesian Optimization—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Show:102550
← PrevPage 137 of 197Next →

Benchmark Results

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