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 601–625 of 1963 papers

TitleStatusHype
Environmental Modeling Framework using Stacked Gaussian Processes—0
Epidemiological Model Calibration via Graybox Bayesian Optimization—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals—0
BOIS: Bayesian Optimization of Interconnected Systems—0
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals—0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Estimation of Riemannian distances between covariance operators and Gaussian processes—0
Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
BrowNNe: Brownian Nonlocal Neurons & Activation Functions—0
Frequency-domain Gaussian Process Models for H_ Uncertainties—0
Evaluation of Deep Gaussian Processes for Text Classification—0
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems—0
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Application of machine learning to gas flaring—0
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding—0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
Fast Kernel Learning for Multidimensional Pattern Extrapolation—0
Show:102550
← PrevPage 25 of 79Next →

Benchmark Results

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