no code implementations • 6 Apr 2023 • Borui Cai, Guangyan Huang, Shuiqiao Yang, Yong Xiang, Chi-Hung Chi
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering.
no code implementations • 22 Mar 2023 • Borui Cai, Yong Xiang, Longxiang Gao, Di wu, He Zhang, Jiong Jin, Tom Luan
To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures.
no code implementations • 13 Mar 2023 • Borui Cai, Shuiqiao Yang, Longxiang Gao, Yong Xiang
Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables.
no code implementations • 16 Jan 2022 • Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, JianXin Li
KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
no code implementations • 15 Apr 2021 • Shuiqiao Yang, Sunny Verma, Borui Cai, Jiaojiao Jiang, Kun Yu, Fang Chen, Shui Yu
Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space.