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Expanding the borders of Multimodal Graph Learning with Sheaf Neural Networks

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If you have a question about this talk, please contact Pietro Lio .

Multimodal graph learning (MGL) has become an emerging topic due to the prevalence of multimodal graphs (MGs). Numerous types of multimodal data are present in a graph format, forming MGs where nodes represent entities of heterogeneous types and edges indicate connections amongst them. A core challenge in MGL lies in effectively processing and integrating knowledge from multiple modalities while navigating the complexities of graph topology. In this talk, I will review the state-of-the-art approaches in MGL and introduce a novel framework: multimodal sheaf neural networks. By attaching a cellular sheaf to a standard multimodal graph, this framework aims to provide enhanced control over modality fusion, opening new avenues for more robust and interpretable learning.

The talk will be streamed:

Join Zoom Meeting https://us05web.zoom.us/j/86434473548?pwd=sK0aBIJPVgZKbWAREyTfccfa2ppRXk.1

Meeting ID: 864 3447 3548 Passcode: 6G6PSh

This talk is part of the Foundation AI series.

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Room FW11 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon SG1 & SG2, Alison Richard Building, 7 West Rd, Cambridge CB3 9DT Engineering Department, Baker Building, Division F meeting room, 5th floor

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