专题:Gaussian Processes and Bayesian Inference

This cluster of papers focuses on the application of Gaussian Processes in machine learning, covering topics such as variational inference, sparse regression, Bayesian inference, deep learning, and probabilistic models. It also explores the use of Gaussian Processes for nonparametric methods, time series modelling, and handling big data.
最新文献
Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression

article Full Text OpenAlex

DSAIS-PINN: Dynamic seeds allocation importance sampling for physics-informed neural networks

article Full Text OpenAlex

Bayesian hierarchical modeling of a multivariate spatiotemporal system based on interdependent physics–informed dynamic processes

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Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayesian inference.

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Counting the number of stationary solutions of partial differential equations via infinite dimensional sampling.

article Full Text OpenAlex

Sparse data-driven random projection in regression for high-dimensional data

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Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies

article Full Text OpenAlex

From clutter to clarity: Emergent neural operators via questionnaire metrics

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Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions

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Simulating Open Quantum Dynamics with a Neural Network-Enhanced Non-Markovian Stochastic Schrödinger Equation

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近5年高被引文献
Physics-informed machine learning

review Full Text OpenAlex 3600 FWCI46.023

Understanding deep learning (still) requires rethinking generalization

article Full Text OpenAlex 1624 FWCI160.351

Auto-Encoding Variational Bayes

article Full Text OpenAlex 950 FWCI167.981

Support Vector Regression

book-chapter Full Text OpenAlex 846 FWCI2.292

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

article Full Text OpenAlex 544 FWCI65.53

PyMC: a modern, and comprehensive probabilistic programming framework in Python

article Full Text OpenAlex 494 FWCI70.692

Optimal ratio for data splitting

article Full Text OpenAlex 473 FWCI64.298

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

review Full Text OpenAlex 316 FWCI16.974

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

article Full Text OpenAlex 292 FWCI36.692

UltraNest - a robust, general purpose Bayesian inference engine

article Full Text OpenAlex 259 FWCI26.042