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

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Showing 1901–1925 of 1963 papers

TitleStatusHype
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite attention: NNGP and NTK for deep attention networks—0
Infinite-channel deep stable convolutional neural networks—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Information fusion in multi-task Gaussian processes—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Information Theoretic Meta Learning with Gaussian Processes—0
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction—0
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
Input Dependent Sparse Gaussian Processes—0
Input Warping for Bayesian Optimization of Non-stationary Functions—0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes—0
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes—0
Inter-domain Deep Gaussian Processes—0
Inter-domain Deep Gaussian Processes with RKHS Fourier Features—0
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Benchmark Results

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