Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Zerui Cai"'
Publikováno v:
Alexandria Engineering Journal, Vol 102, Iss , Pp 279-289 (2024)
Energy consumption optimization is crucial for improving the quality of application services in the industrial Internet of Things (IIoT) environment. Traditional optimization methods often adopt blind search strategies, which leads to a significant w
Externí odkaz:
https://doaj.org/article/16beeff479924f2b9814db260a4f1a64
Autor:
Zefeng Cai, Zerui Cai
Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Autor:
Zefeng Cai, Zerui Cai
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Conditional Variational AutoEncoder (CVAE) is promising for modeling one-to-many relationships in dialogue generation, as it can naturally generate many responses from a given context. However, the conventional used continual latent variables in CVAE
Autor:
Zerui Cai
Publikováno v:
2021 IEEE International Conference on Data Mining (ICDM).
Autor:
Xiaofeng He, Chengguang Tang, Peng Li, Minghui Qiu, Jun Huang, Taolin Zhang, Zerui Cai, Chengyu Wang, Yang Li
Publikováno v:
CIKM
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the language understanding abilities of deep language models by leveraging the rich semantic knowledge from knowledge graphs, other than plain pre-training texts. However, previous effor
Publikováno v:
ACL/IJCNLP (1)
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51ff299d6251989d32b34e980ef4da31
http://arxiv.org/abs/2108.08983
http://arxiv.org/abs/2108.08983
Publikováno v:
Web and Big Data ISBN: 9783030858957
APWeb/WAIM (1)
APWeb/WAIM (1)
Medical text mining aims to learn models to extract useful information from medical sources. A major challenge is obtaining large-scale labeled data in the medical domain for model training, which is highly expensive. Recent studies show that leverag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d55cd26641a4c5fdaddf56c9b04f9160
https://doi.org/10.1007/978-3-030-85896-4_20
https://doi.org/10.1007/978-3-030-85896-4_20
Publikováno v:
ACL/IJCNLP (Findings)