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 676–700 of 1963 papers

TitleStatusHype
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance LearningCode0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Learning Choice Functions with Gaussian ProcessesCode0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
On the role of Model Uncertainties in Bayesian Optimization—0
Modeling the evolution of temporal knowledge graphs with uncertainty—0
Machine learning methods for prediction of breakthrough curves in reactive porous media—0
Robust Bayesian Target Value Optimization—0
Application of machine learning to gas flaring—0
Quantile Autoregression-based Non-causality Testing—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Robust and Scalable Gaussian Process Regression and Its ApplicationsCode0
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential EquationsCode0
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Benchmark Results

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