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 1501–1550 of 1963 papers

TitleStatusHype
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimizationCode0
Scalable GAM using sparse variational Gaussian processes—0
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Recursive Estimation of Dynamic RSS Fields Based on Crowdsourcing and Gaussian Processes—0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Evaluating the squared-exponential covariance function in Gaussian processes with integral observationsCode0
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Physics-Based Learning for Robotic Environmental Sensing—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
The Limitations of Model Uncertainty in Adversarial Settings—0
Temporal alignment and latent Gaussian process factor inference in population spike trains—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Deep Factors with Gaussian Processes for Forecasting—0
Neural Non-Stationary Spectral KernelCode0
Sequence Alignment with Dirichlet Process Mixtures—0
Robust Super-Level Set Estimation using Gaussian Processes—0
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference—0
Infinite-Horizon Gaussian ProcessesCode0
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes—0
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions—0
Unifying Probabilistic Models for Time-Frequency AnalysisCode0
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Gaussian Process Conditional Density Estimation—0
Scaling Gaussian Process Regression with DerivativesCode0
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization BoundsCode0
Variational Calibration of Computer Models—0
Adversarially Robust Optimization with Gaussian Processes—0
A Gaussian Process perspective on Convolutional Neural Networks—0
Scalable Gaussian Processes on Discrete Domains—0
Data Association with Gaussian Processes—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Non-linear process convolutions for multi-output Gaussian processes—0
A General Framework for Fair Regression—0
Harmonizable mixture kernels with variational Fourier features—0
Deep learning with differential Gaussian process flowsCode0
A Hybrid Approach for Trajectory Control Design—0
Deep convolutional Gaussian processesCode0
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU AccelerationCode0
Orthogonally Decoupled Variational Gaussian ProcessesCode0
Modeling longitudinal data using matrix completionCode0
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities—0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
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Benchmark Results

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