Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Songfang Huang"'
Autor:
Jinkun Zeng, Yaoyun Zhang, Yutao Xiang, Sugai Liang, Chuang Xue, Junhang Zhang, Ya Ran, Minne Cao, Fei Huang, Songfang Huang, Wei Deng, Tao Li
Publikováno v:
npj Mental Health Research, Vol 2, Iss 1, Pp 1-11 (2023)
Abstract There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarke
Externí odkaz:
https://doaj.org/article/e968747588594b89bcaecf8e875eb2f8
Publikováno v:
JMIR Medical Informatics, Vol 10, Iss 12, p e40743 (2022)
BackgroundUnder the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available cli
Externí odkaz:
https://doaj.org/article/8c5a71463d4f4234a4068d053307e43c
Autor:
Ming Yan, Haiyang Xu, Chenliang Li, Junfeng Tian, Bin Bi, Wei Wang, Xianzhe Xu, Ji Zhang, Songfang Huang, Fei Huang, Luo Si, Rong Jin
Publikováno v:
ACM Transactions on Information Systems. 41:1-40
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last dec
Publikováno v:
SC22: International Conference for High Performance Computing, Networking, Storage and Analysis.
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:4555-4563
Pretrained language models have recently advanced a wide range of natural language processing tasks. Nowadays, the application of pretrained language models to IR tasks has also achieved impressive results. Typical methods either directly apply a pre
BACKGROUND Under the paradigm of Precision Medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33bb837fa7112e3460113e59bcc4c1e3
https://doi.org/10.2196/preprints.40743
https://doi.org/10.2196/preprints.40743
Autor:
Menglin Lu, Yaoyun Zhang, Junhang Zhang, Songfang Huang, Fei Huang, Tingna Wang, Fei Wu, Hongjing Mao, Zhengxing Huang
Publikováno v:
JAMA Network Open. 6:e237597
ImportanceAlthough digital cognitive behavioral therapy for insomnia (dCBT-I) has been studied in many randomized clinical trials and is recommended as a first-line treatment option, few studies have systematically examined its effectiveness, engagem
Autor:
Fei Huang, Mosha Chen, Chuanqi Tan, Ningyu Zhang, Songfang Huang, Hongbin Ye, Shumin Deng, Huajun Chen
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 29:3077-3088
Information extraction tasks such as entity relation extraction and event extraction are of great importance for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end information extraction task for se
Autor:
Zhanyong Tang, Songfang Huang, Yansong Feng, Shin Hwei Tan, Huanting Wang, Lizhong Bian, Dingyi Fang, Guixin Ye, Zheng Wang
Publikováno v:
IEEE Transactions on Information Forensics and Security. 16:1943-1958
This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel learning framework for building vulnerability detection models. Funded leverages the advances in graph neural networks (GNNs) to develop a novel graph-based learning m
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigates three aspects of structured pruning on multilingual pre-trained lan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d39c3ce9736b57535be6a2c482e9a58d
http://arxiv.org/abs/2204.02601
http://arxiv.org/abs/2204.02601