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of 37
pro vyhledávání: '"Zhang, Minxing"'
The increasing cost of training machine learning (ML) models has led to the inclusion of new parties to the training pipeline, such as users who contribute training data and companies that provide computing resources. This involvement of such new par
Externí odkaz:
http://arxiv.org/abs/2408.00129
Autor:
Zhang, Minxing, Pei, Jian
Data markets serve as crucial platforms facilitating data discovery, exchange, sharing, and integration among data users and providers. However, the paramount concern of privacy has predominantly centered on protecting privacy of data owners and thir
Externí odkaz:
http://arxiv.org/abs/2407.09771
Autor:
Xie, Roy, Wang, Junlin, Huang, Ruomin, Zhang, Minxing, Ge, Rong, Pei, Jian, Gong, Neil Zhenqiang, Dhingra, Bhuwan
The rapid scaling of large language models (LLMs) has raised concerns about the transparency and fair use of the pretraining data used for training them. Detecting such content is challenging due to the scale of the data and limited exposure of each
Externí odkaz:
http://arxiv.org/abs/2406.15968
Generative models have demonstrated revolutionary success in various visual creation tasks, but in the meantime, they have been exposed to the threat of leaking private information of their training data. Several membership inference attacks (MIAs) h
Externí odkaz:
http://arxiv.org/abs/2310.19410
This paper revisits the robust overfitting phenomenon of adversarial training. Observing that models with better robust generalization performance are less certain in predicting adversarially generated training inputs, we argue that overconfidence in
Externí odkaz:
http://arxiv.org/abs/2310.04539
In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster
Externí odkaz:
http://arxiv.org/abs/2306.00012
Autor:
Zhang, Minxing, Ren, Zhaochun, Wang, Zihan, Ren, Pengjie, Chen, Zhumin, Hu, Pengfei, Zhang, Yang
Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender sys
Externí odkaz:
http://arxiv.org/abs/2109.08045
Publikováno v:
EPJ Web of Conferences, Vol 295, p 11025 (2024)
Large-scale high-energy physics experiments generate scientific data at the scale of petabytes or even exabytes, requiring high-performance data IO for processing. However, in large computing centers, computing and storage devices are typically separ
Externí odkaz:
https://doaj.org/article/ebffc78170ba41679ffaace1219af474
Akademický článek
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Top gate engineering of field-effect transistors based on wafer-scale two-dimensional semiconductors
Autor:
Ma, Jingyi, Chen, Xinyu, Sheng, Yaochen, Tong, Ling, Guo, Xiaojiao, Zhang, Minxing, Luo, Chen, Zong, Lingyi, Xia, Yin, Sheng, Chuming, Wang, Yin, Gou, Saifei, Wang, Xinyu, Wu, Xing, Zhou, Peng, Zhang, David Wei, Wu, Chenjian, Bao, Wenzhong
Publikováno v:
In Journal of Materials Science & Technology 20 April 2022 106:243-248