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of 3 949
pro vyhledávání: '"XU, Heng"'
Autor:
Wang, Zhengli, Cao, Shunshun, Lu, Jiguang, Liu, Yulan, Shi, Xun, Jiang, Jinchen, Liang, Enwei, Wang, Weiyang, Xu, Heng, Xu, Renxin
We report the detection of an extreme flux decrease accompanied by clear dispersion measure (DM) and rotation measure (RM) variations for pulsar B1929+10 during the 110-minute radio observation with the Five-hundred-meter Aperture Spherical radio Tel
Externí odkaz:
http://arxiv.org/abs/2410.16816
Machine unlearning, a process enabling pre-trained models to remove the influence of specific training samples, has attracted significant attention in recent years. While extensive research has focused on developing efficient unlearning strategies, t
Externí odkaz:
http://arxiv.org/abs/2410.10120
Autor:
Zhi, Yuxing, Guo, Yuan, Yuan, Kai, Wang, Hesong, Xu, Heng, Yao, Haina, Yang, Albert C, Huang, Guangrui, Duan, Yuping
Background: Large language models (LLMs) have seen extraordinary advances with applications in clinical decision support. However, high-quality evidence is urgently needed on the potential and limitation of LLMs in providing accurate clinical decisio
Externí odkaz:
http://arxiv.org/abs/2409.14478
Learned Image Compression (LIC) models have achieved superior rate-distortion performance than traditional codecs. Existing LIC models use CNN, Transformer, or Mixed CNN-Transformer as basic blocks. However, limited by the shifted window attention, S
Externí odkaz:
http://arxiv.org/abs/2409.14090
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine unlearning, which is required when the effect of some specific trainin
Externí odkaz:
http://arxiv.org/abs/2406.12516
Autor:
Wu, Ziwei, Zhu, Weiwei, Zhang, Bing, Feng, Yi, Han, JinLin, Li, Di, Li, Dongzi, Luo, Rui, Niu, Chenhui, Niu, Jiarui, Wang, Bojun, Wang, Fayin, Wang, Pei, Wang, Weiyang, Xu, Heng, Yang, Yuanpei, Zhang, Yongkun, Zhou, Dejiang, Zhu, Yuhao, Deng, Can-Min, Xu, Yonghua
We present the scintillation velocity measurements of FRB~20201124A from the FAST observations, which reveal an annual variation. This annual variation is further supported by changes detected in the scintillation arc as observed from the secondary s
Externí odkaz:
http://arxiv.org/abs/2406.12218
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, have resulted in this need to allow a trained model to forget some of it
Externí odkaz:
http://arxiv.org/abs/2406.10954
Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategies. However, the verification of machine unlearning, or in other words,
Externí odkaz:
http://arxiv.org/abs/2406.10953
Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a cluster of instances or all instances belonging to a specific class. Thes
Externí odkaz:
http://arxiv.org/abs/2406.10951
In current AI era, users may request AI companies to delete their data from the training dataset due to the privacy concerns. As a model owner, retraining a model will consume significant computational resources. Therefore, machine unlearning is a ne
Externí odkaz:
http://arxiv.org/abs/2405.15662