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 551–575 of 1963 papers

TitleStatusHype
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Dynamic Online Ensembles of Basis ExpansionsCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple KernelCode0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentialsCode0
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical ApplicationsCode0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep LearningCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural NetworksCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
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