Graph neural networks: Variations and applications
Many real-world tasks require understanding interactions between a set of entities. Examples include interacting atoms in chemical molecules, people in social networks and even syntactic interactions between tokens in program source code. Graph structured data types are a natural representation for such systems, and several architectures have been proposed for applying deep learning methods to these structured objects. I will give an overview of the research directions inside Microsoft that have explored different architectures and applications for deep learning on graph structured data. See more at https://www.microsoft.com/en-us/resea...
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An Introduction to Graph Neural Networks: Models and Applications
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Understanding Graph Attention Networks
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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
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Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models
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Gradient descent, how neural networks learn | Deep Learning Chapter 2
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ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein
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Optics for the Cloud PhD Event 2020 - Day 1
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Visualizing transformers and attention | Talk for TNG Big Tech Day '24
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Representational Power of Graph Neural Networks - Stefanie Jegelka
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Graph Neural Networks
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Transformers, the tech behind LLMs | Deep Learning Chapter 5
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How Deep Neural Networks Work
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Deep learning on graphs: successes, challenges | Graph Neural Networks | Michael Bronstein
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Yann LeCun - Graph Embedding, Content Understanding, and Self-Supervised Learning
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Spectral Graph Theory For Dummies
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Graph Convolutional Networks (GCNs) made simple
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Intro to graph neural networks (ML Tech Talks)
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The Most Important Algorithm in Machine Learning
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Geometric Deep Learning on Graphs and Manifolds - #NIPS2017
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