1 code implementation • 15 Nov 2022 • Zheng Wang, Mingrui Liu, Cheng Long, Qianru Zhang, Jiangneng Li, Chunyan Miao
The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level.
no code implementations • 6 Sep 2022 • Jiaxing Xu, Jianbin Cui, Jiangneng Li, Wenge Rong, Noboru Matsuda
One of the main challenges is to collect a sufficient amount of annotated data to train a model.
1 code implementation • 13 Sep 2021 • Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
no code implementations • 18 Nov 2020 • Ziniu Wu, Rong Zhu, Andreas Pfadler, Yuxing Han, Jiangneng Li, Zhengping Qian, Kai Zeng, Jingren Zhou
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).