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 1751–1775 of 1963 papers

TitleStatusHype
Environmental Modeling Framework using Stacked Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Variational Fourier features for Gaussian processesCode0
The Recycling Gibbs Sampler for Efficient Learning—0
Faster variational inducing input Gaussian process classification—0
Gaussian Processes for Survival Analysis—0
Stochastic Variational Deep Kernel Learning—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes—0
GPflow: A Gaussian process library using TensorFlowCode2
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps—0
Learning Scalable Deep Kernels with Recurrent StructureCode0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)—0
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation—0
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields—0
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Random Feature Expansions for Deep Gaussian ProcessesCode0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act ClassificationCode0
Appraisal of data-driven and mechanistic emulators of nonlinear hydrodynamic urban drainage simulators—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
No-Regret Replanning under Uncertainty—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Show:102550
← PrevPage 71 of 79Next →

Benchmark Results

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