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 1351–1400 of 1963 papers

TitleStatusHype
Partial Separability and Functional Graphical Models for Multivariate Gaussian ProcessesCode0
A Learnable Safety MeasureCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Probabilistic Deep Ordinal Regression Based on Gaussian Processes—0
Non-Gaussian processes and neural networks at finite widths—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Localizing and Amortizing: Efficient Inference for Gaussian Processes—0
Disentangling Trainability and Generalization in Deep Learning—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Kalman Filtering with Gaussian Processes Measurement Noise—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Bayesian Optimisation with Gaussian Processes for Premise Selection—0
No-Regret Learning in Unknown Games with Correlated PayoffsCode0
Compositional uncertainty in deep Gaussian processesCode0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Coarse-scale PDEs from fine-scale observations via machine learning—0
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases -- Extended Version—0
Finite size corrections for neural network Gaussian processes—0
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal DynamicsCode0
Using Contextual Information to Improve Blood Glucose Prediction—0
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal DimensionCode0
Stochastic data-driven model predictive control using Gaussian processes—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Sequential Learning of Active SubspacesCode0
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs—0
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators—0
Structured Variational Inference in Unstable Gaussian Process State Space ModelsCode0
The Use of Gaussian Processes in System Identification—0
Gaussian Processes for Analyzing Positioned Trajectories in Sports—0
The Debiased Spatial Whittle LikelihoodCode0
Learning GPLVM with arbitrary kernels using the unscented transformationCode0
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation—0
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-timeCode0
Modulating Surrogates for Bayesian Optimization—0
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN—0
Modeling Severe Traffic Accidents With Spatial And Temporal Features—0
Sequential Neural ProcessesCode0
Compositionally-Warped Gaussian Processes—0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Black-Box Inference for Non-Linear Latent Force Models—0
Multi-resolution Multi-task Gaussian ProcessesCode0
Bayesian Learning from Sequential Data using Gaussian Processes with Signature CovariancesCode0
Learning Directed Graphical Models from Gaussian Data—0
Recurrent Neural ProcessesCode0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Kernelized Capsule NetworksCode0
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Benchmark Results

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