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 851–875 of 1963 papers

TitleStatusHype
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Group Importance Sampling for Particle Filtering and MCMC—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Harmonizable mixture kernels with variational Fourier features—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Healing Gaussian Process Experts—0
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Incremental Ensemble Gaussian Processes—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
Deep banach space kernels—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records—0
Aligned Multi-Task Gaussian Process—0
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Efficient Determination of Safety Requirements for Perception Systems—0
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Benchmark Results

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