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pro vyhledávání: '"Huang, Zhangcheng"'
Text summarization is essential for information aggregation and demands large amounts of training data. However, concerns about data privacy and security limit data collection and model training. To eliminate this concern, we propose a federated lear
Externí odkaz:
http://arxiv.org/abs/2304.11524
Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once trained, the model remembers the data. Even when data is removed from t
Externí odkaz:
http://arxiv.org/abs/2209.15276
The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology. Several deep learning-based techniques have recently been introduced
Externí odkaz:
http://arxiv.org/abs/2209.15181
Autor:
Li, Denghao, Zeng, Yuqiao, Wang, Jianzong, Kong, Lingwei, Huang, Zhangcheng, Cheng, Ning, Qu, Xiaoyang, Xiao, Jing
Buddhism is an influential religion with a long-standing history and profound philosophy. Nowadays, more and more people worldwide aspire to learn the essence of Buddhism, attaching importance to Buddhism dissemination. However, Buddhist scriptures w
Externí odkaz:
http://arxiv.org/abs/2209.15164
Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologies, the differential privacy (DP) is th
Externí odkaz:
http://arxiv.org/abs/2206.03492
Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing
Externí odkaz:
http://arxiv.org/abs/2205.14643
Autor:
Li, Shuowen, Gao, Yunhui, Wu, Jiachen, Wang, Mingjie, Huang, Zhangcheng, Chen, Shumei, Cao, Liangcai
Publikováno v:
In Fundamental Research March 2024
Federated learning has made an important contribution to data privacy-preserving. Many previous works are based on the assumption that the data are independently identically distributed (IID). As a result, the model performance on non-identically ind
Externí odkaz:
http://arxiv.org/abs/2009.07455
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