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
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Temporal alignment and latent Gaussian process factor inference in population spike trains—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
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes—0
Infinite-Horizon Gaussian ProcessesCode0
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
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization BoundsCode0
Variational Calibration of Computer Models—0
Scaling Gaussian Process Regression with DerivativesCode0
A Gaussian Process perspective on Convolutional Neural Networks—0
Adversarially Robust Optimization with Gaussian Processes—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
A General Framework for Fair Regression—0
Harmonizable mixture kernels with variational Fourier features—0
Non-linear process convolutions for multi-output Gaussian processes—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
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
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Benchmark Results

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