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  1. Wang Fuli (Chino: 王 馥 荔, 12 de noviembre de 1949 ) es una actriz china . Biografía. Wang nació en Xuzhou, provincia de Jiangsu y su ciudad natal y ancestral es Tianjin. Se graduó de la universidad de Jiangsu en 1967, especializándose después en el coservatorio de ópera de Pekín.

  2. en.wikipedia.org › wiki › Wang_FuliWang Fuli - Wikipedia

    Wang Fuli (Chinese: 王馥荔; born 12 November 1949) is a Chinese actress. Wang was born in Xuzhou, Jiangsu Province, and her ancestral hometown was Tianjin. She graduated from Jiangsu Drama College in 1967, majoring in Peking Opera. Wang later served as an actress of Jiangsu Provincial Peking Opera Troupe.

  3. www.imdb.com › name › nm0298121Fuli Wang - IMDb

    Fuli Wang was born on 22 October 1949 in Xuzhou, Jiangsu, China. She is an actress, known for Ri Chu (1986), Legend of Tianyun Mountain (1980) and Zan men de niu bai sui (1983).

  4. Fuli Wang received the B.E., M.E., and Ph.D. degrees in control theory and control engineering from Northeastern University, Shenyang, China, in 1982, 1985, and 1988, respectively. He is currently a Professor with the College of Information Science and Engineering, Northeastern University.

  5. Research. T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations. Proposed a hypergraph neural network framework based on a tensor representation of the hypergraph structure. The paper is under a minor revision to IEEE Transaction on Neural Networks and Learning Systems.

  6. 20 de may. de 2019 · In this work, we propose to integrate the user-item interactions -- more specifically the bipartite graph structure -- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it.

  7. 21 de oct. de 2020 · Self-supervised Graph Learning for Recommendation. Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie. Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors.