no code implementations • 9 Sep 2023 • Lingling Tang, Jiangtao Hu, Hua Yu, Surui Liu, Jielei Chu
To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability.
no code implementations • 9 Nov 2020 • Yu Wang, Shu Jiang, Weiman Lin, Yu Cao, Longtao Lin, Jiangtao Hu, Jinghao Miao, Qi Luo
This paper presents the design of a tune-free (human-out-of-the-loop parameter tuning) control framework, aiming at accelerating large scale autonomous driving system deployed on various vehicles and driving environments.
no code implementations • Knowledge-Based Systems, 105916. 2020 • Yan Zhang, Hua Xu, Yunfeng Xu, Junhui Deng, Juan Gu, Rui Ma, Jie Lai, Jiangtao Hu, Xiaoshuai Yu, Lei Hou, Lidong Gu, Yanling Wei, Yichao Xiao, Junhao Lu
In this paper, we try to give a more visual and detailed definition of structural hole spanner based on the existing work, and propose a novel algorithm to identify structural hole spanner based on community forest model and diminishing marginal utility.
no code implementations • 29 Sep 2019 • Jiacheng Pan, Hongyi Sun, Kecheng Xu, Yifei Jiang, Xiangquan Xiao, Jiangtao Hu, Jinghao Miao
The practicability and interpretability analysis of the model shows great potential for large-scale deployment in various autonomous driving systems in addition to our own.
1 code implementation • 20 Jul 2018 • Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.