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This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
While simple approximations to the bbox are trivial (such as computing the bounding box of their control points), in this article we deduce the exact bounding box analytically.
U. Martin Skrodzki, and K. Polthier. Proceedings of Bridges 2016: Mathematics, Music, Art, Architecture, Education, Culture, page 481--484. Phoenix, Arizona, Tessellations Publishing, (2016)Available online at http://archive.bridgesmathart.org/2016/bridges2016-481.html.
C. Gunn. (2014)cite arxiv:1411.6502Comment: 25 pages, 4 figures in Advances in Applied Clifford Algebras, pages 1--24, 2016, online at link.springer.com.
A. Chéritat. (2014)cite arxiv:1410.4417Comment: 16 pages, 7 figures. This version has the following changes: Added computer generated images of the key positions S1 and S2. Corrected several minor mistakes. Corrected the proof of the main proposition (I had forgotten to ensure that the top and bottom curves remain embedded during the homotopy) and slightly changed the statement of Lemma 3 to adapt.
R. Sharipov. (2004)cite arxiv:math/0405323Comment: The textbook, AmSTeX, 143 pages, amsppt style, prepared for double side printing on letter size paper.