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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 711–720 of 1963 papers

TitleStatusHype
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
On boundary conditions parametrized by analytic functions—0
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Bézier Curve Gaussian Processes—0
Probabilistic Models for Manufacturing Lead Times—0
Know Thy Student: Interactive Learning with Gaussian Processes—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN—0
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Benchmark Results

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