Zobrazeno 1 - 10
of 4 838
pro vyhledávání: '"SU, Chang"'
In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minim
Externí odkaz:
http://arxiv.org/abs/2411.04423
Semantic parsing that translates natural language queries to SPARQL is of great importance for Knowledge Graph Question Answering (KGQA) systems. Although pre-trained language models like T5 have achieved significant success in the Text-to-SPARQL tas
Externí odkaz:
http://arxiv.org/abs/2410.05731
Autor:
Geng, Xiang, Zhu, Ming, Li, Jiahuan, Lai, Zhejian, Zou, Wei, She, Shuaijie, Guo, Jiaxin, Zhao, Xiaofeng, Li, Yinglu, Li, Yuang, Su, Chang, Zhao, Yanqing, Lyu, Xinglin, Zhang, Min, Chen, Jiajun, Yang, Hao, Huang, Shujian
The scarcity of non-English data limits the development of non-English large language models (LLMs). Transforming English-centric LLMs to non-English has been identified as an effective and resource-efficient method. Previous works start from base LL
Externí odkaz:
http://arxiv.org/abs/2405.13923
Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes. However, most of the existing approaches for this purpose require access to samples with complete modality avail
Externí odkaz:
http://arxiv.org/abs/2405.11280
Autor:
Zhao, Haofei, Liu, Yilun, Tao, Shimin, Meng, Weibin, Chen, Yimeng, Geng, Xiang, Su, Chang, Zhang, Min, Yang, Hao
Publikováno v:
2024 International Joint Conference on Neural Networks (IJCNN)
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT. After two decades of evol
Externí odkaz:
http://arxiv.org/abs/2403.14118
Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals'
Externí odkaz:
http://arxiv.org/abs/2403.08599
Autor:
Hao, Zhongkai, Su, Chang, Liu, Songming, Berner, Julius, Ying, Chengyang, Su, Hang, Anandkumar, Anima, Song, Jian, Zhu, Jun
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings. However, it is largely in its infancy due to the inherent complexity and diversity, such as long trajectories, multiple
Externí odkaz:
http://arxiv.org/abs/2403.03542
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their
Externí odkaz:
http://arxiv.org/abs/2402.00531
Autor:
Guo, Jiaxin, Wang, Minghan, Qiao, Xiaosong, Wei, Daimeng, Shang, Hengchao, Li, Zongyao, Yu, Zhengzhe, Li, Yinglu, Su, Chang, Zhang, Min, Tao, Shimin, Yang, Hao
Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Pai
Externí odkaz:
http://arxiv.org/abs/2401.05689
Autor:
Liu, Yilun, Tao, Shimin, Zhao, Xiaofeng, Zhu, Ming, Ma, Wenbing, Zhu, Junhao, Su, Chang, Hou, Yutai, Zhang, Miao, Zhang, Min, Ma, Hongxia, Zhang, Li, Yang, Hao, Jiang, Yanfei
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions. The quality of instruction pairs used for tuning greatly affects the performance of LLMs. However, the manual creation of high-quality ins
Externí odkaz:
http://arxiv.org/abs/2311.13246