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 801–825 of 1963 papers

TitleStatusHype
Multitask Gaussian Process with Hierarchical Latent Interactions—0
Generalized Twin Gaussian Processes using Sharma-Mittal Divergence—0
Damage detection in operational wind turbine blades using a new approach based on machine learning—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Linear-time inference for Gaussian Processes on one dimension—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
A Perspective on Gaussian Processes for Earth Observation—0
Bayesian Variational Optimization for Combinatorial Spaces—0
A computationally lightweight safe learning algorithm—0
Global optimization using Gaussian Processes to estimate biological parameters from image data—0
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I—0
Global Optimization with Parametric Function Approximation—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Efficient Global Optimization using Deep Gaussian Processes—0
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
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Benchmark Results

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