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 701–725 of 1963 papers

TitleStatusHype
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches—0
A Chain Rule for the Expected Suprema of Bernoulli Processes—0
Dependence between Bayesian neural network units—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
Deep Transformed Gaussian Processes—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Deep Sigma Point Processes—0
Bayesian estimation of orientation preference maps—0
Analogical-based Bayesian Optimization—0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Deep Random Splines for Point Process Intensity Estimation—0
Quantum neural networks form Gaussian processes—0
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes—0
Adaptive Inducing Points Selection For Gaussian Processes—0
A chain rule for the expected suprema of Gaussian processes—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Amortized Variational Inference for Deep Gaussian Processes—0
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Benchmark Results

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