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
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian Process for Trajectories—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Gaussian Process Latent Class Choice Models—0
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology—0
Gaussian Process Molecule Property Prediction with FlowMO—0
Gaussian Process Morphable Models—0
Gaussian Process Neurons—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
Gaussian Process on the Product of Directional Manifolds—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Volatility Model—0
Gauss-Legendre Features for Gaussian Process Regression—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Linear-time inference for Gaussian Processes on one dimension—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Global Optimization of Gaussian processes—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Global Optimization with Parametric Function Approximation—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
GP Kernels for Cross-Spectrum Analysis—0
Bayesian Nonparametric Dimensionality Reduction of Categorical Data for Predicting Severity of COVID-19 in Pregnant Women—0
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes—0
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Gradient-enhanced deep Gaussian processes for multifidelity modelling—0
Granger Causality from Quantized Measurements—0
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Benchmark Results

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