Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ye, Xucheng"'
Autor:
Hu, Yulan, Li, Qingyang, Ouyang, Sheng, Chen, Ge, Chen, Kaihui, Mei, Lijun, Ye, Xucheng, Zhang, Fuzheng, Liu, Yong
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses generated. A critical component of RLHF is the reward model, which is train
Externí odkaz:
http://arxiv.org/abs/2406.16486
Autor:
Fu, Jiayi, Lin, Lei, Gao, Xiaoyang, Liu, Pengli, Chen, Zhengzong, Yang, Zhirui, Zhang, Shengnan, Zheng, Xue, Li, Yan, Liu, Yuliang, Ye, Xucheng, Liao, Yiqiao, Liao, Chao, Chen, Bin, Song, Chengru, Wan, Junchen, Lin, Zijia, Zhang, Fuzheng, Wang, Zhongyuan, Zhang, Di, Gai, Kun
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we intr
Externí odkaz:
http://arxiv.org/abs/2310.07488
Autor:
Zhao, Yinglin, Yang, Jianlei, Li, Bing, Cheng, Xingzhou, Ye, Xucheng, Wang, Xueyan, Jia, Xiaotao, Wang, Zhaohao, Zhang, Youguang, Zhao, Weisheng
The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM) architectu
Externí odkaz:
http://arxiv.org/abs/2204.09989
Publikováno v:
In Results in Engineering December 2024 24
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and c
Externí odkaz:
http://arxiv.org/abs/2108.09081
Publikováno v:
IEEE Transactions on Computers, 2021
Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computin
Externí odkaz:
http://arxiv.org/abs/2106.07894
Pre-trained language models achieve outstanding performance in NLP tasks. Various knowledge distillation methods have been proposed to reduce the heavy computation and storage requirements of pre-trained language models. However, from our observation
Externí odkaz:
http://arxiv.org/abs/2106.03613
Autor:
Dai, Pengcheng, Yang, Jianlei, Ye, Xucheng, Cheng, Xingzhou, Luo, Junyu, Song, Linghao, Chen, Yiran, Zhao, Weisheng
Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity. It mainly involves three levels of
Externí odkaz:
http://arxiv.org/abs/2007.13595
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed. Actually, an important observation shows that most
Externí odkaz:
http://arxiv.org/abs/1908.00173
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