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 476–500 of 1963 papers

TitleStatusHype
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Quantum neural networks form Gaussian processes—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Deep Random Splines for Point Process Intensity Estimation—0
Architectures and random properties of symplectic quantum circuits—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Sigma Point Processes—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Deep Transformed Gaussian Processes—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Dependence between Bayesian neural network units—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Design of Experiments for Verifying Biomolecular Networks—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Collaborative Gaussian Processes for Preference Learning—0
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Benchmark Results

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