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 726–750 of 1963 papers

TitleStatusHype
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Meta-Learning Mean Functions for Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models—0
Deep Manifold Prior—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Bayesian Anomaly Detection and Classification—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Deep kernel processes—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets—0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Deep Importance Sampling based on Regression for Model Inversion and Emulation—0
Deep Horseshoe Gaussian Processes—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Deep Gaussian Processes with Convolutional Kernels—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Bayesian active learning for choice models with deep Gaussian processes—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Deep Gaussian Processes for geophysical parameter retrieval—0
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Benchmark Results

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