Topological Deep Learning: Going Beyond Graph Data
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore topological deep learning in this comprehensive lecture on developing deep learning models for data supported on topological domains. Dive into efficient message passing over long ranges, neighborhood functions, and bridging gaps between higher-order domains. Learn about CC-Embeddings for enriching domains and the differences between continuous and discrete neighborhoods. Gain insights into Combinatorial Complex Neural Networks, including CC-Pooling and CC-Unpooling techniques. Examine practical applications through mesh segmentation results before participating in a Q&A session. Discover how this unifying deep learning framework built on rich data structures can be applied to simplicial complexes, cell complexes, and hypergraphs, generalizing many domains encountered in scientific computations.