Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Tian Chunlin"'
Publikováno v:
Applied Artificial Intelligence, Vol 38, Iss 1 (2024)
We, the Editor and Publisher of Applied Artificial Intelligence, have retracted the following article:Zhang Maoyun, Xi Huizhuang, Tang Chen, Jiang Yuheng, Zhang Ziyan & Tian Chunlin, Binding Mechanism of Aviation Wire Harness Based on Improved Partic
Externí odkaz:
https://doaj.org/article/06c0a0b2b0dc4693bac03726c99c2ccc
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deplo
Externí odkaz:
http://arxiv.org/abs/2410.11577
Publikováno v:
2024 IEEE/ACM International Symposium on Quality of Service (IWQoS)
Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumption during the training process excludes the low-end devices from cont
Externí odkaz:
http://arxiv.org/abs/2409.07202
Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, intensive memory footprint during the training process severely bottlenecks the d
Externí odkaz:
http://arxiv.org/abs/2408.10826
Autor:
Liao, Haicheng, Sun, Haoyu, Shen, Huanming, Wang, Chengyue, Tam, Kahou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substantial challenges stemming from the unpredictable nature of traffic acci
Externí odkaz:
http://arxiv.org/abs/2407.17757
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Guan, Yanchen, Tam, KaHou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditional accident anticipation models primarily utilizing dashcam videos ar
Externí odkaz:
http://arxiv.org/abs/2407.16277
Autor:
Liao, Haicheng, Li, Yongkang, Li, Zhenning, Wang, Chengyue, Tian, Chunlin, Huang, Yuming, Bian, Zilin, Zhu, Kaiqun, Li, Guofa, Pu, Ziyuan, Hu, Jia, Cui, Zhiyong, Xu, Chengzhong
Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to i
Externí odkaz:
http://arxiv.org/abs/2407.07020
Autor:
Ning, Zhiyuan, Tian, Chunlin, Xiao, Meng, Fan, Wei, Wang, Pengyang, Li, Li, Wang, Pengfei, Zhou, Yuanchun
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods,
Externí odkaz:
http://arxiv.org/abs/2405.06312
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency,
Externí odkaz:
http://arxiv.org/abs/2405.04122
Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fin
Externí odkaz:
http://arxiv.org/abs/2404.19245