Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Zeng, Rongfei"'
Autor:
Tang, Zhenheng, Kang, Xueze, Yin, Yiming, Pan, Xinglin, Wang, Yuxin, He, Xin, Wang, Qiang, Zeng, Rongfei, Zhao, Kaiyong, Shi, Shaohuai, Zhou, Amelie Chi, Li, Bo, He, Bingsheng, Chu, Xiaowen
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system designed and implemented for training DNNs using geo-distributed GPUs acros
Externí odkaz:
http://arxiv.org/abs/2410.12707
Autor:
Tang, Zhenheng, Wang, Yuxin, He, Xin, Zhang, Longteng, Pan, Xinglin, Wang, Qiang, Zeng, Rongfei, Zhao, Kaiyong, Shi, Shaohuai, He, Bingsheng, Chu, Xiaowen
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs. However, consumer-level GPUs, which
Externí odkaz:
http://arxiv.org/abs/2309.01172
Autor:
Li, Ying, Wang, Xingwei, Zeng, Rongfei, Donta, Praveen Kumar, Murturi, Ilir, Huang, Min, Dustdar, Schahram
Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is
Externí odkaz:
http://arxiv.org/abs/2306.01334
Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression fo
Externí odkaz:
http://arxiv.org/abs/2206.00447
Publikováno v:
In Computer Networks September 2024 251
Publikováno v:
In Future Generation Computer Systems August 2024 157:485-498
Publikováno v:
In Future Generation Computer Systems June 2024 155:121-131
Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated wit
Externí odkaz:
http://arxiv.org/abs/2106.15406
Autor:
Zhou, Chenhong, Liu, Feng, Gong, Chen, Zeng, Rongfei, Liu, Tongliang, Cheung, William K., Han, Bo
Publikováno v:
Transactions on Machine Learning Research, 2023
In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image, where labeled training images and unlabeled test images are from the same distribution and share the same label set. However, in a
Externí odkaz:
http://arxiv.org/abs/2106.06237
Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security aspects, bu
Externí odkaz:
http://arxiv.org/abs/2002.09699