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#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]

Machine Learning Street Talk (MLST)

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Enhancing Graph Neural Networks

This chapter explores the advancements in Graph Neural Networks (GNNs) through equivariant subgraph aggregation, addressing current limitations in expressive power. The discussion connects concepts from physics and geometry to machine learning, highlighting the importance of symmetry in improving model efficiency and understanding complex data structures.

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