no code implementations • 8 Dec 2022 • Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, Hao Yang
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.
no code implementations • 2 Dec 2022 • Xiaoting Li, Yuhang Wu, Vineeth Rakesh, Yusan Lin, Hao Yang, Fei Wang
Graph neural networks have achieved significant success in representation learning.
no code implementations • 13 Jan 2022 • Lan Wang, Yusan Lin, Yuhang Wu, Huiyuan Chen, Fei Wang, Hao Yang
Today's cyber-world is vastly multivariate.
no code implementations • 15 Aug 2021 • Yuhang Wu, Mengting Gu, Lan Wang, Yusan Lin, Fei Wang, Hao Yang
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.
no code implementations • 13 May 2020 • Maryam Moosaei, Yusan Lin, Hao Yang
There are a few approaches that consider an entire outfit, but these approaches have limitations such as requiring rich semantic information, category labels, and fixed order of items.
no code implementations • 16 Jul 2019 • Yusan Lin, Hao Yang
Fashion is a large and fast-changing industry.