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pro vyhledávání: '"Ying, Chenhao"'
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explo
Externí odkaz:
http://arxiv.org/abs/2404.12186
Autor:
Long, Shaocong, Zhou, Qianyu, Li, Xiangtai, Lu, Xuequan, Ying, Chenhao, Luo, Yuan, Ma, Lizhuang, Yan, Shuicheng
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic compl
Externí odkaz:
http://arxiv.org/abs/2404.07794
Domain generalization(DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invarian
Externí odkaz:
http://arxiv.org/abs/2309.16483
Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making i
Externí odkaz:
http://arxiv.org/abs/2309.16460
Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mecha
Externí odkaz:
http://arxiv.org/abs/2305.16793
Publikováno v:
In Applied Energy 15 October 2024 372
Autor:
Feng, Huifen, Zhang, Weiting, Liu, Ying, Zhang, Chuan, Ying, Chenhao, Jin, Jian, Jiao, Zhenzhen
Publikováno v:
In Computer Networks April 2024 242
Locally repairable codes (LRCs), which can recover any symbol of a codeword by reading only a small number of other symbols, have been widely used in real-world distributed storage systems, such as Microsoft Azure Storage and Ceph Storage Cluster. Si
Externí odkaz:
http://arxiv.org/abs/2105.11271
Publikováno v:
In Information Sciences July 2023 636
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