Hands-on Geometric Deep Learning

Hands-on Geometric Deep Learning

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June 2025

Mastering Special Orthogonal Groups With Practice
Lie Special Orthogonal Groups SO(n) are essential in machine learning when dealing with rotational symmetries, enabling models to be more robust…
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Patrick Nicolas
Shape Your Models with The Fisher-Rao Metric
Data often possesses underlying shapes and geometric structures that can be harnessed to model data distributions. The Fisher–Rao metric exemplifies…
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Patrick Nicolas
Geometry of Closed-Form Statistical Manifolds
Machine learning models grounded in information geometry can be challenging due to the intractability of their geometric properties. This article, the…
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Patrick Nicolas
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May 2025

Quiz Yourself on Geometric Deep Learning
Got questions?  We’ve got 170+ sharp answers, covering Graph Neural Networks, Topology &  Manifold learning!
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Patrick Nicolas
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SE(3): The Lie Group That Moves the World
After years of feeling daunted by Lie groups and algebras, I finally took the plunge into exploring these fascinating smooth manifolds.
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Patrick Nicolas
Neighbors Matter: How Homophily Shapes Graph Neural Networks
Given the wide range of configuration choices, setting up a Graph Neural Network can be daunting. Node and edge homophily ratios might offer a criterion…
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Patrick Nicolas

April 2025

March 2025

Demystifying Graph Sampling & Walk Methods
The versatility of graph representations makes them highly valuable for solving a wide range of problems, each with its own unique data structure. This…
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Patrick Nicolas
Taming Symmetry: A Dive into Lie Groups with Python
Lie groups play a crucial role in Geometric Deep Learning by modeling symmetries such as rotation, translation, and scaling.
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Patrick Nicolas
Taming PyTorch Geometric for Graph Neural Networks
Overwhelmed by the functionality and complexity of the PyTorch Geometric API? Gain a foundational understanding of PyTorch Geometric and learn how to…
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Patrick Nicolas
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