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 1851–1875 of 1963 papers

TitleStatusHype
Graph Classification Gaussian Processes via Spectral Features—0
Graph Classification Gaussian Processes via Hodgelet Spectral Features—0
Graph Convolutional Gaussian Processes—0
Graph Convolutional Gaussian Processes For Link Prediction—0
Genus expansion for non-linear random matrix ensembles with applications to neural networks—0
Graphical LASSO Based Model Selection for Time Series—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Grouped Gaussian Processes for Solar Power Prediction—0
Group Importance Sampling for Particle Filtering and MCMC—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I—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
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—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
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
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
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Benchmark Results

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