Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Zhang, Mengshi"'
Autor:
Ma, Dongning, Jiao, Xun, Lin, Fred, Zhang, Mengshi, Desmaison, Alban, Sellinger, Thomas, Moore, Daniel, Sankar, Sriram
Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS ar
Externí odkaz:
http://arxiv.org/abs/2307.10244
Ensuring the safety and robustness of autonomous driving systems (ADSs) is imperative. One of the crucial methods towards this assurance is the meticulous construction and execution of test scenarios, a task often regarded as tedious and laborious. I
Externí odkaz:
http://arxiv.org/abs/2305.06018
When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to the system. However, simply replaying the entire recording is not necess
Externí odkaz:
http://arxiv.org/abs/2209.01546
Autor:
Gao, Yanan, Ma, Danyue, Li, Mengchao, Zhang, Mengshi, Chen, Yunbo, Han, Changmi, Wang, Yutian, Fang, Xiujie, Dou, Yao, Wang, Kun
Publikováno v:
In Measurement January 2025 242 Part B
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neur
Externí odkaz:
http://arxiv.org/abs/2106.12233
Program repair is an integral part of every software system's life-cycle but can be extremely challenging. To date, researchers have proposed various automated program repair (APR) techniques to reduce efforts of manual debugging. However, given a re
Externí odkaz:
http://arxiv.org/abs/2104.04611
Autor:
Xu, Chenhao, Ge, Jiaqi, Li, Yong, Deng, Yao, Gao, Longxiang, Zhang, Mengshi, Xiang, Yong, Zheng, Xi
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but struggles with non-iid datasets. Va
Externí odkaz:
http://arxiv.org/abs/2103.07050
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Akademický článek
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Publikováno v:
In Journal of the Franklin Institute November 2023 360(16):11892-11915