Zobrazeno 1 - 10
of 356
pro vyhledávání: '"Luo, Junyu"'
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair b
Externí odkaz:
http://arxiv.org/abs/2406.09031
Autor:
Ju, Wei, Wang, Yifan, Qin, Yifang, Mao, Zhengyang, Xiao, Zhiping, Luo, Junyu, Yang, Junwei, Gu, Yiyang, Wang, Dongjie, Long, Qingqing, Yi, Siyu, Luo, Xiao, Zhang, Ming
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challen
Externí odkaz:
http://arxiv.org/abs/2405.11868
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of
Externí odkaz:
http://arxiv.org/abs/2402.15700
Autor:
Wang, Jiaqi, Luo, Junyu, Ye, Muchao, Wang, Xiaochen, Zhong, Yuan, Chang, Aofei, Huang, Guanjie, Yin, Ziyi, Xiao, Cao, Sun, Jimeng, Ma, Fenglong
The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With
Externí odkaz:
http://arxiv.org/abs/2402.01077
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural networks demonstrate proficiency in modeling this type of data, their success
Externí odkaz:
http://arxiv.org/abs/2402.00447
Autor:
Wang, Xiaochen, Luo, Junyu, Wang, Jiaqi, Yin, Ziyi, Cui, Suhan, Zhong, Yuan, Wang, Yaqing, Ma, Fenglong
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail
Externí odkaz:
http://arxiv.org/abs/2310.07871
The prevalent use of large language models (LLMs) in various domains has drawn attention to the issue of "hallucination," which refers to instances where LLMs generate factually inaccurate or ungrounded information. Existing techniques for hallucinat
Externí odkaz:
http://arxiv.org/abs/2309.02654
Autor:
Luo, Junyu, Fu, Jiahui, Kong, Xianghao, Gao, Chen, Ren, Haibing, Shen, Hao, Xia, Huaxia, Liu, Si
3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description. Previous methods mostly follow a two-stage paradigm, i.e., language-irrelevant detection and cross-modal matching, w
Externí odkaz:
http://arxiv.org/abs/2204.06272
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments. To evaluate their robustness, existing research conducts adversarial attacks in the white/gray-box setting where model par
Externí odkaz:
http://arxiv.org/abs/2112.06063
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and c
Externí odkaz:
http://arxiv.org/abs/2108.09081