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 876–900 of 1963 papers

TitleStatusHype
Gaussian Process Volatility Model—0
Current Methods for Drug Property Prediction in the Real World—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—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
Gaussian Process Regression for Maximum Entropy Distribution—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Correcting Model Bias with Sparse Implicit Processes—0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
Gaussian Process Optimization with Mutual Information—0
Gaussian Process on the Product of Directional Manifolds—0
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Benchmark Results

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