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
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes—0
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems—0
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders—0
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic CharacterizationCode0
When are Iterative Gaussian Processes Reliably Accurate?Code0
Rough multifactor volatility for SPX and VIX options—0
GPEX, A Framework For Interpreting Artificial Neural NetworksCode0
Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow—0
Learning Rigidity-based Flocking Control with Gaussian Processes—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
A Sparse Expansion For Deep Gaussian Processes—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
A Bayesian take on option pricing with Gaussian processes—0
Data Fusion with Latent Map Gaussian Processes—0
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes—0
Structure-Aware Random Fourier Kernel for Graphs—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
Continuous-time edge modelling using non-parametric point processes—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
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Benchmark Results

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