A collection of 800+ resources for developers presented in curated lists. Learn programming, find a new job, discover your next favourite podcast, improve your workflow and a lot more.
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
Physics is a part of games that has always amazed me. I find it funny how impossible it seemed to do correctly when I was younger. While making a custom game engine, it was finally demystified!
The full article: https://blog.winter.dev/2020/designing-a-physics-engine/
The background game demo: https://winter.dev/demo
J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. (2021)cite arxiv:2105.04026Comment: This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University Press.
R. Hanocka, G. Metzer, R. Giryes, and D. Cohen-Or. (2020)cite arxiv:2005.11084Comment: SIGGRAPH 2020; Project page: https://ranahanocka.github.io/point2mesh/.
K. Stelzner, K. Kersting, and A. Kosiorek. (2021)cite arxiv:2104.01148Comment: 15 pages, 3 figures. For project page with videos, see http://stelzner.github.io/obsurf/.
J. Solà, J. Deray, and D. Atchuthan. (2018)cite arxiv:1812.01537Comment: 17 pages, 12 figures, 7 boxed examples, 193 numbered equations. V2 add chapter with a application examples. V3 fix biblio error and remove the reference to a not-yet-published library in C++. V4 add again the reference to the C++ library "manif", which is made available with this version 4. V5 fix formulas (163) and (179). V6, V7 fix typos. V8 fix sign in eq 149.
L. Wang, Y. Zhao, Y. Jinnai, Y. Tian, and R. Fonseca. (2018)cite arxiv:1805.07440Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020).