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Activity Number: 392 - Big Tensor Data Analysis
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #309515
Title: Duality of Graphical Models and Tensor Networks
Author(s): Elina Robeva*
Companies: University of British Columbia

We show a simple duality correspondence between tensor networks and graphical models. We study tensor networks on hypergraphs which we call tensor hypernetworks. In this sense the information of a tensor hypernetwork corresponding to a given hypergraph is exactly the same as a discrete graphical model on the dual hypergraph. We translate various notions under duality. For example, marginalization in a graphical model is the same as contraction in the corresponding tensor network. Algorithms also translate under duality. This simple correspondence is a reminder that the fields of graphical models and tensor networks could benefit from interaction.

Authors who are presenting talks have a * after their name.

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