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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 426–450 of 1963 papers

TitleStatusHype
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
A brief note on understanding neural networks as Gaussian processes—0
Deep banach space kernels—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Aligned Multi-Task Gaussian Process—0
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding—0
Deep Compositional Spatial Models—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Deep Ensemble Kernel Learning—0
Bandits for Learning to Explain from Explanations—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Deep Gaussian Covariance Network—0
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Design of Experiments for Verifying Biomolecular Networks—0
Deep Gaussian Processes: A Survey—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
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Benchmark Results

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