📚 Weekend Reading

This week brought quite a few interesting papers and resources - we encourage you to invest there some time:

Geometric multimodal representation learning by Yasha Ektefaie, George Dasoulas, Ayush Noori, Maha Farhat, and Marinka Zitnik. A survey of 100+ papers on graphs combined with other modalities and a framework of multi-modal approaches for natural sciences like physical interaction, molecular reasoning, and protein modeling.

Clifford Neural Layers for PDE Modeling by Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta. If you thought you know all the basics from the Geometric Deep Learning Course - here is something more challenging. The authors introduce the ideas from Geometric Algebra into ML tasks, namely, Clifford Algebras that unify numbers, vectors, complex numbers, quaternions, and have additional primitives to incorporate plane and volume segments. The paper gives a great primer on the math and applications. You can also watch a very visual YouTube lecture on Geometric Algebras.

Categories for AI (Cats4AI) - an upcoming open course on Category Theory created by Andrew Dudzik, Bruno Gavranović, João Guilherme Araújo, Petar Veličković, and Pim de Haan. “This course is aimed towards machine learning researchers, but approachable to anyone with a basic understanding of linear algebra and differential calculus. The material is self-contained and all the necessary background will be introduced along the way.” Don’t forget your veggies 🥦