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 1551–1600 of 1963 papers

TitleStatusHype
Learning-based attacks in cyber-physical systems—0
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze—0
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product NetworksCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Efficient Global Optimization using Deep Gaussian Processes—0
Gait learning for soft microrobots controlled by light fields—0
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes—0
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I—0
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in DrosophilaCode0
Inter-state switching in stochastic gene expression: Exact solution, an adiabatic limit and oscillations in molecular distributions—0
Learning Invariances using the Marginal Likelihood—0
Deep Convolutional Networks as shallow Gaussian ProcessesCode0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Multi-Output Convolution Spectral Mixture for Gaussian Processes—0
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Assessing Quality Estimation Models for Sentence-Level Prediction—0
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Remote sensing image regression for heterogeneous change detection—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces—0
On Lebesgue Integral Quadrature—0
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes—0
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient MatchingCode0
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture—0
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning—0
Fully Scalable Gaussian Processes using Subspace Inducing Inputs—0
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm DesignCode0
Probabilistic Bisection with Spatial Metamodels—0
Bayesian Deep Learning on a Quantum ComputerCode0
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees—0
Neural-net-induced Gaussian process regression for function approximation and PDE solution—0
Stagewise Safe Bayesian Optimization with Gaussian Processes—0
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte CarloCode0
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Grouped Gaussian Processes for Solar Power Prediction—0
Variational Implicit ProcessesCode0
Deep Gaussian Processes with Convolutional Kernels—0
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Wrapped Gaussian Process Regression on Riemannian Manifolds—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Dirichlet-based Gaussian Processes for Large-scale Calibrated ClassificationCode0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive VarianceCode0
Efficient Inference in Multi-task Cox Process ModelsCode0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
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Benchmark Results

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