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 1651–1700 of 1963 papers

TitleStatusHype
Distributed non-parametric deep and wide networks—0
Sparse Covariance Modeling in High Dimensions with Gaussian Processes—0
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distributionCode0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes—0
Scalable Levy Process Priors for Spectral Kernel Learning—0
Gaussian process based nonlinear latent structure discovery in multivariate spike train data—0
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models—0
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease ProgressionCode0
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian ProcessesCode0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Joint Gaussian Processes for Biophysical Parameter Retrieval—0
Model Criticism in Latent SpaceCode0
GPflowOpt: A Bayesian Optimization Library using TensorFlowCode0
Scalable Log Determinants for Gaussian Process Kernel LearningCode0
Structured Variational Inference for Coupled Gaussian Processes—0
Modelling Representation Noise in Emotion Analysis using Gaussian Processes—0
Learning Kernels over Strings using Gaussian Processes—0
Deep Neural Networks as Gaussian ProcessesCode0
Tensor Regression Meets Gaussian Processes—0
Auto-Differentiating Linear Algebra—0
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train DecompositionCode0
Deep Gaussian Covariance Network—0
Safe Learning of Quadrotor Dynamics Using Barrier Certificates—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes—0
Remote Sensing Image Classification with Large Scale Gaussian Processes—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Morphable Face Models - An Open FrameworkCode0
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs—0
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes—0
Perturbative Black Box Variational Inference—0
Analogical-based Bayesian Optimization—0
Forecasting of commercial sales with large scale Gaussian Processes—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Learning from lions: inferring the utility of agents from their trajectories—0
Convolutional Gaussian ProcessesCode2
Spectral Mixture Kernels for Multi-Output Gaussian Processes—0
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
Pillar Networks++: Distributed non-parametric deep and wide networks—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential EquationsCode0
Bayesian Optimization with Tree-structured Dependencies—0
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Robust Bayesian Optimization with Student-t Likelihood—0
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Benchmark Results

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