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Found 3 papers, 2 papers with code
Explaining latent representations of generative models with large multimodal models
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge.
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