Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Lejian Liao"'
Publikováno v:
iScience, Vol 27, Iss 8, Pp 110509- (2024)
Summary: Magnetic resonance imaging (MRI), ultrasound (US), and contrast-enhanced ultrasound (CEUS) can provide different image data about uterus, which have been used in the preoperative assessment of endometrial cancer. In practice, not all the pat
Externí odkaz:
https://doaj.org/article/0725a49d26e347a7b9d79de7b202b512
Publikováno v:
IEEE Access, Vol 8, Pp 49212-49223 (2020)
Development of internet of things (IoT) and smart devices eased life by offering numerous applications targeting to provide real-time low latency services, but they also brought challenges in handling huge data generated from the powerful computation
Externí odkaz:
https://doaj.org/article/0a5a6ccdfa6243728840f90c4f10d207
Publikováno v:
IEEE Access, Vol 8, Pp 184318-184338 (2020)
The measurement of semantic similarity between concepts is an important research topic in natural language processing. In the past, several approaches for measuring the semantic similarity between concepts have been proposed based on WordNet or Wikip
Externí odkaz:
https://doaj.org/article/cbe3035756f1429085abe9dea75285bb
Publikováno v:
Applied Sciences, Vol 12, Iss 7, p 3437 (2022)
Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and s
Externí odkaz:
https://doaj.org/article/09982e6a47fa4d179d5fdd9f00d1c275
Publikováno v:
Neurocomputing. 501:25-40
Autor:
Tesfayee Meshu Welde, Lejian Liao
Publikováno v:
Artificial Intelligence Review.
Publikováno v:
Neurocomputing. 465:26-37
Named entity recognition (NER) is a fundamental problem in natural language processing. In particular, nested entities are commonly existed in real-life textual data for the NER task. However, the current span-based methods for nested NER are computa
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding per
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65a8b6ecd9920eac44a7ae158e767c51
http://arxiv.org/abs/2212.06423
http://arxiv.org/abs/2212.06423
Autor:
Meihuizi Jia, Xin Shen, Lei Shen, Jinhui Pang, Lejian Liao, Yang Song, Meng Chen, Xiaodong He
Publikováno v:
Proceedings of the 30th ACM International Conference on Multimedia.
Akademický článek
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