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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 526–550 of 1963 papers

TitleStatusHype
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Additive Gaussian Processes RevisitedCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Functional Variational Bayesian Neural NetworksCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Federated Causal Inference from Observational DataCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Function-Space Distributions over KernelsCode0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
Additive Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
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Benchmark Results

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