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 1401–1425 of 1963 papers

TitleStatusHype
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Local Granger Causality—0
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization—0
Localizing and Amortizing: Efficient Inference for Gaussian Processes—0
Locally adaptive factor processes for multivariate time series—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Locally induced Gaussian processes for large-scale simulation experiments—0
Locally Smoothed Gaussian Process Regression—0
Local Model Feature Transformations—0
Location Dependent Dirichlet Processes—0
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Longitudinal Deep Kernel Gaussian Process Regression—0
Low-pass filtering as Bayesian inference—0
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes—0
Machine learning based hyperspectral image analysis: A survey—0
Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers—0
Machine Learning for a Low-cost Air Pollution Network—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling—0
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Benchmark Results

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