Zobrazeno 1 - 10
of 1 228
pro vyhledávání: '"WANG Shengjie"'
Autor:
WANG Shengjie, ZHANG Qinghong
Publikováno v:
Dianxin kexue, Vol 40, Pp 121-133 (2024)
In the telecom industry, accurate prediction of customer churn is crucial for the companies involved to maintain market competitiveness and increase revenue. To this end, a customer churn prediction framework combining CatBoost algorithm and SHAP mod
Externí odkaz:
https://doaj.org/article/6fd55b0470aa42e6b502ee09d5d24b17
Autor:
Zhu, Jun, Du, Zihao, Xu, Haotian, Lan, Fengbo, Zheng, Zilong, Ma, Bo, Wang, Shengjie, Zhang, Tao
Task-aware navigation continues to be a challenging area of research, especially in scenarios involving open vocabulary. Previous studies primarily focus on finding suitable locations for task completion, often overlooking the importance of the robot
Externí odkaz:
http://arxiv.org/abs/2407.09053
Foundation models pre-trained on web-scale data are shown to encapsulate extensive world knowledge beneficial for robotic manipulation in the form of task planning. However, the actual physical implementation of these plans often relies on task-speci
Externí odkaz:
http://arxiv.org/abs/2403.08248
Space robots have played a critical role in autonomous maintenance and space junk removal. Multi-arm space robots can efficiently complete the target capture and base reorientation tasks due to their flexibility and the collaborative capabilities bet
Externí odkaz:
http://arxiv.org/abs/2403.08219
Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to real-world tasks. While recent algorithms have made significant strides in improving sample efficiency, none have achieved consistently superior performance acro
Externí odkaz:
http://arxiv.org/abs/2403.00564
Diffusion models, capable of high-quality image generation, receive unparalleled popularity for their ease of extension. Active users have created a massive collection of domain-specific diffusion models by fine-tuning base models on self-collected d
Externí odkaz:
http://arxiv.org/abs/2312.08873
Autor:
Lan, Fengbo, Wang, Shengjie, Zhang, Yunzhe, Xu, Haotian, Oseni, Oluwatosin, Zhang, Ziye, Gao, Yang, Zhang, Tao
Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the potential to
Externí odkaz:
http://arxiv.org/abs/2310.08809
Autor:
Ye, Weirui, Zhang, Yunsheng, Weng, Haoyang, Gu, Xianfan, Wang, Shengjie, Zhang, Tong, Wang, Mengchen, Abbeel, Pieter, Gao, Yang
Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of inter
Externí odkaz:
http://arxiv.org/abs/2310.02635
Establishing the correspondences between newly acquired points and historically accumulated data (i.e., map) through nearest neighbors search is crucial in numerous robotic applications. However, static tree data structures are inadequate to handle l
Externí odkaz:
http://arxiv.org/abs/2309.08315
Autor:
Bukharin, Alexander, Liu, Tianyi, Wang, Shengjie, Zuo, Simiao, Gao, Weihao, Yan, Wen, Zhao, Tuo
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research. Even for the most data-efficient MLFFs, reaching chemical accuracy can
Externí odkaz:
http://arxiv.org/abs/2306.03109