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 826–850 of 1963 papers

TitleStatusHype
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
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
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
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Inducing Gaussian Process Networks—0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Discovering and forecasting extreme events via active learning in neural operators—0
Autoencoder Attractors for Uncertainty EstimationCode0
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs—0
Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety—0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data—0
Position Tracking using Likelihood Modeling of Channel Features with Gaussian Processes—0
A Bayesian Approach for Shaft Centre Localisation in Journal Bearings—0
Modelling variability in vibration-based PBSHM via a generalised population form—0
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel—0
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
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Benchmark Results

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