GRAPHICAL MODELS IN MACHINE LEARNING, NETWORKS AND UNCERTAINTY QUANTIFICATION

DOI10.1142/9789813272880_0204OpenAlexW2972481208MaRDI QIDQ5122180

Andrea L. Bertozzi

Publication date: 22 September 2020

Published in: Proceedings of the International Congress of Mathematicians (ICM 2018) (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/c295ee17db06243e437f16e50e8a3fc9fa488f68

zbMATH Keywords

social networksmachine learninggraphical modelsdiffuse interfacesmodularitydata clusteringcommunity detectionuncertainty quantificationgraph LaplacianMBO scheme

Mathematics Subject Classification ID

Clustering in the social and behavioral sciences (91C20) Social networks; opinion dynamics (91D30) Numerical optimization and variational techniques (65K10) Proceedings, conferences, collections, etc. pertaining to computer science (68-06) Numerical methods of relaxation type (49M20) Ginzburg-Landau equations (35Q56)

Related Items (5)

Graphical models for statistical inference and data assimilation ⋮ Model Uncertainty and Correctability for Directed Graphical Models ⋮ Graphical Models and Message-Passing Algorithms: Some Introductory Lectures ⋮ Learning Graphical Models From the Glauber Dynamics ⋮ Probabilistic graphical models. Principles and applications

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