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 1801–1850 of 1963 papers

TitleStatusHype
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
A flexible state space model for learning nonlinear dynamical systems—0
Structured and Efficient Variational Deep Learning with Matrix Gaussian PosteriorsCode0
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining—0
The Multivariate Generalised von Mises distribution: Inference and applications—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
An analytic comparison of regularization methods for Gaussian Processes—0
State Space representation of non-stationary Gaussian Processes—0
Facility Deployment Decisions through Warp Optimizaton of Regressed Gaussian Processes—0
Probabilistic Programming with Gaussian Process Memoization—0
Quantum assisted Gaussian process regression—0
Bayesian Model Adaptation for Crowd Counts—0
Multi-Conditional Latent Variable Model for Joint Facial Action Unit Detection—0
GP Kernels for Cross-Spectrum Analysis—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
The Automatic Statistician: A Relational Perspective—0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
Recurrent Gaussian ProcessesCode0
Variational Auto-encoded Deep Gaussian Processes—0
Complex-Valued Gaussian Processes for Regression—0
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces—0
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation—0
Deep Kernel LearningCode1
Thoughts on Massively Scalable Gaussian ProcessesCode0
Gaussian Process Random FieldsCode0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
The Human Kernel—0
Optimization as Estimation with Gaussian Processes in Bandit SettingsCode0
Optimization for Gaussian Processes via Chaining—0
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting—0
Fast Gaussian Process Regression for Big Data—0
Modeling and interpolation of the ambient magnetic field by Gaussian processes—0
Semi-described and semi-supervised learning with Gaussian processes—0
Steps Toward Deep Kernel Methods from Infinite Neural Networks—0
Learning Structural Kernels for Natural Language Processing—0
String and Membrane Gaussian Processes—0
Fast Adaptive Weight Noise—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes—0
MCMC for Variationally Sparse Gaussian Processes—0
Mondrian Forests for Large-Scale Regression when Uncertainty MattersCode0
Provable Bayesian Inference via Particle Mirror Descent—0
String Gaussian Process Kernels—0
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models—0
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Optimal change point detection in Gaussian processes—0
Batch Bayesian Optimization via Local PenalizationCode0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes—0
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Benchmark Results

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