no code implementations • 2 Feb 2024 • Mengdan Zhu, Zhenke Liu, Bo Pan, Abhinav Angirekula, Liang Zhao
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.
1 code implementation • 17 Dec 2023 • Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao
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.
1 code implementation • NeurIPS Workshop AI4Scien 2021 • Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao
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.