Zobrazeno 1 - 6
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pro vyhledávání: '"Libin Hou"'
Dense Convolutional Network has been continuously refined to adopt a highly efficient and compact architecture, owing to its lightweight and efficient structure. However, the current Dense-like architectures are mainly designed manually, it becomes i
Externí odkaz:
http://arxiv.org/abs/2410.07499
Publikováno v:
Brain Sciences, Vol 12, Iss 12, p 1633 (2022)
Research on visual encoding models for functional magnetic resonance imaging derived from deep neural networks, especially CNN (e.g., VGG16), has been developed. However, CNNs typically use smaller kernel sizes (e.g., 3 × 3) for feature extraction i
Externí odkaz:
https://doaj.org/article/f1a202aab5f74d15b38636c8116b516b
Publikováno v:
Human Brain and Artificial Intelligence ISBN: 9789811982217
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae93d8912d02b79fefdade10c422f047
https://doi.org/10.1007/978-981-19-8222-4_3
https://doi.org/10.1007/978-981-19-8222-4_3
Autor:
Libin Hou, Donghui Cui
Publikováno v:
6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9b31aaa723398fd91ea211280939f48
https://doi.org/10.1117/12.2653190
https://doi.org/10.1117/12.2653190
Publikováno v:
2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9249ae83779293d2aa44ddd2cb4bd21
https://doi.org/10.1109/icmipe53131.2021.9698894
https://doi.org/10.1109/icmipe53131.2021.9698894
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642363368
CLSW
CLSW
Event argument extraction is an important component of event extraction which plays a decisive role in whether event extraction can be applied to the actual. This paper proposes a method of event argument extraction based on Conditional Random Fields
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa096e5f2a96834cff1ecaccaaa0624a
https://doi.org/10.1007/978-3-642-36337-5_4
https://doi.org/10.1007/978-3-642-36337-5_4