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 1676–1700 of 1963 papers

TitleStatusHype
Learning Kernels over Strings using Gaussian Processes—0
Deep Neural Networks as Gaussian ProcessesCode0
Modelling Representation Noise in Emotion Analysis using Gaussian Processes—0
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
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
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Benchmark Results

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