Sep 02 2024 29 mins 2
Graph representation learning has recently become one of the hottest topics in machine learning.
One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design.
Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision.
In this Michael Bronstein outlines his views on the possible reasons and how the field could progress in the next few years.
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Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales.
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