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 351–375 of 1963 papers

TitleStatusHype
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learningCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
Estimation of Dynamic Gaussian ProcessesCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Gaussian Process Random FieldsCode0
Entropic Trace Estimates for Log DeterminantsCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsCode0
Considering discrepancy when calibrating a mechanistic electrophysiology modelCode0
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Constant-Time Predictive Distributions for Gaussian ProcessesCode0
Challenges in Gaussian Processes for Non Intrusive Load MonitoringCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Evolving-Graph Gaussian ProcessesCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
Chained Gaussian ProcessesCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Show:102550
← PrevPage 15 of 79Next →

Benchmark Results

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