3 papers indexed
Ying Yan, Liwei Hu, Xiaoming Zhang
Existing generative models learn data distributions in flat Euclidean space. However, most data in our real world are manifolds embedded in high dimensional Euclidean space. Therefore, we propose an intrinsic-geometry-based generative adversarial network (IG-GAN) for data generat…
Zhaoyan Sun, Shan Zhong, Daizhou Wen, Jiaxing Han, Guoliang Li, Ying Yan, et al.
Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-dri…
Xuekai Wang, Wenke Liu, Wang Yuying, Qingsong Wan, Wenbang Hao, Xiya Peng, et al.
Abstract Rapid advancements in autonomous driving technology are reshaping the automotive industry, making vehicle intelligence and road safety critical benchmarks of sector-wide progress. However, due to the challenges in obtaining crash data, existing research on the severity a…