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 451–475 of 1963 papers

TitleStatusHype
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Deep Gaussian Processes with Convolutional Kernels—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Combining Parametric Land Surface Models with Machine Learning—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version—0
Deep kernel processes—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Deep Manifold Prior—0
Meta-Learning Mean Functions for Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Architectures and random properties of symplectic quantum circuits—0
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Benchmark Results

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