Zobrazeno 1 - 10
of 11 349
pro vyhledávání: '"HUANG, Sheng"'
Active learning (AL) has achieved great success by selecting the most valuable examples from unlabeled data. However, they usually deteriorate in real scenarios where open-set noise gets involved, which is studied as open-set annotation (OSA). In thi
Externí odkaz:
http://arxiv.org/abs/2409.17607
Autor:
Kao, Yung-Cheng, Huang, Sheng-Hsuan, Chang, Chin-Hsuan, Wu, Chih-Hsiang, Chu, Shih-Hsien, Jiang, Jian, Zhang, An-Chi, Huang, Sheng-Yao, Yan, Jhih-Heng, Feng, Kai-Ming, Chuu, Chih-Sung
Publikováno v:
Opt. Express 31, 30239-30247 (2023)
Quantumkey distribution (QKD) promises unconditional security for communication. However, the random choices of the measurement basis in QKD usually result in low key creation efficiency. This drawback is overcome in the differential-phase-shift QKD,
Externí odkaz:
http://arxiv.org/abs/2406.07786
In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreaking attempt explores the integration of quantum encoders within the tr
Externí odkaz:
http://arxiv.org/abs/2408.13919
Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification
Externí odkaz:
http://arxiv.org/abs/2408.08125
Large language models (LLMs) have shown great potential in code-related tasks, yet open-source models lag behind their closed-source counterparts. To bridge this performance gap, existing methods generate vast amounts of synthetic data for fine-tunin
Externí odkaz:
http://arxiv.org/abs/2408.02193
Existing technologies for distributed light-field mapping and light pollution monitoring (LPM) rely on either remote satellite imagery or manual light surveying with single-point sensors such as SQMs (sky quality meters). These modalities offer low-r
Externí odkaz:
http://arxiv.org/abs/2408.00808
Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label
Externí odkaz:
http://arxiv.org/abs/2407.18624
Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from
Externí odkaz:
http://arxiv.org/abs/2407.17689
Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the teacher network. However, introducin
Externí odkaz:
http://arxiv.org/abs/2407.03719
Autor:
Chen, Zhao-Yun, Ma, Teng-Yang, Ye, Chuang-Chao, Xu, Liang, Tan, Ming-Yang, Zhuang, Xi-Ning, Xu, Xiao-Fan, Wang, Yun-Jie, Sun, Tai-Ping, Chen, Yong, Du, Lei, Guo, Liang-Liang, Zhang, Hai-Feng, Tao, Hao-Ran, Wang, Tian-Le, Yang, Xiao-Yan, Zhao, Ze-An, Wang, Peng, Zhang, Sheng, Zhang, Chi, Zhao, Ren-Ze, Jia, Zhi-Long, Kong, Wei-Cheng, Dou, Meng-Han, Wang, Jun-Chao, Liu, Huan-Yu, Xue, Cheng, Zhang, Peng-Jun-Yi, Huang, Sheng-Hong, Duan, Peng, Wu, Yu-Chun, Guo, Guo-Ping
Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterati
Externí odkaz:
http://arxiv.org/abs/2406.06063