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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 876–900 of 1963 papers

TitleStatusHype
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Deep Ensemble Kernel Learning—0
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
How to turn your camera into a perfect pinhole model—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Deep Factors with Gaussian Processes for Forecasting—0
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes—0
Efficient Determination of Safety Requirements for Perception Systems—0
Deep Gaussian Covariance Network—0
Matching models across abstraction levels with Gaussian Processes—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Bayesian Relational Generative Model for Scalable Multi-modal Learning—0
Identifying Causal Direction via Variational Bayesian Compression—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
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Benchmark Results

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