Gaussian Processes
574 papers with code • 1 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Gaussian Processes
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Libraries
Use these libraries to find Gaussian Processes models and implementations
UCI Machine Learning Repository
MPI FAUST Dataset
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Latest papers
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
navid-ziaei/ldgd
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This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold.
Simulation Based Bayesian Optimization
BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
NeelKanwal/Vision-Transformers-for-Small-Histological-Datasets-Learned-Through-Knowledge-Distillation
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Computerized Medical Imaging and Graphics 2023
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
billzxl/dil-gp
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We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor.
GP+: A Python Library for Kernel-based learning via Gaussian Processes
bostanabad-research-group/gp-plus
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In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions.
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
llnl/gplasdi
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Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs).
Estimation of Dynamic Gaussian Processes
Gaussian processes provide a compact representation for modeling and estimating an unknown function, that can be updated as new measurements of the function are obtained.
Gaussian Processes for Monitoring Air-Quality in Kampala
claramst/gps-kampala-airquality
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Monitoring air pollution is of vital importance to the overall health of the population.
Spatial Bayesian Neural Networks
andrewzm/sbnn
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We propose several variants of SBNNs, most of which are able to match the finite-dimensional distribution of the target process at the selected grid better than conventional BNNs of similar complexity.
