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Faculties & Institutes
Faculty of Scien... [3]
THE STATE KEY LA... [1]
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VONG CHI MAN [2]
WONG FAI [1]
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Conference paper [1]
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2024 [3]
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英語English [3]
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Neural Networks [1]
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Federated learning using model projection for multi-center disease diagnosis with non-IID data
Journal article
Du, Jie, Li, Wei, Liu, Peng, Vong, Chi Man, You, Yongke, Lei, Baiying, Wang, Tianfu. Federated learning using model projection for multi-center disease diagnosis with non-IID data[J]. Neural Networks, 2024, 178, 106409.
Authors:
Du, Jie
;
Li, Wei
;
Liu, Peng
;
Vong, Chi Man
;
You, Yongke
; et al.
Favorite
|
TC[WOS]:
0
TC[Scopus]:
0
IF:
6.0
/
7.9
|
Submit date:2024/07/04
Catastrophic Forgetting
Federated Learning
Invalid Aggregation
Multi-center Disease Diagnosis
Non-iid Data
Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System
Journal article
Jie Du, Peng Liu, Chi Man Vong, Chuangquan Chen, Tianfu Wang, C. L.P. Chen. Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(8), 11332-11345.
Authors:
Jie Du
;
Peng Liu
;
Chi Man Vong
;
Chuangquan Chen
;
Tianfu Wang
; et al.
Favorite
|
TC[WOS]:
7
TC[Scopus]:
6
IF:
10.2
/
10.4
|
Submit date:2023/08/03
Broad Learning System (Bls)
Catastrophic Forgetting
Class Correlations
Class-incremental Learning (Cil)
Recursive Update Rule
MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation
Conference paper
Pang, Jianhui, Yang, Baosong, Wong, Derek Fai, Liu, Dayiheng, Wei, Xiangpeng, Xie, Jun, Chao, Lidia Sam. MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation[C]:European Language Resources Association (ELRA) and ICCL, 2024, 11560-11573.
Authors:
Pang, Jianhui
;
Yang, Baosong
;
Wong, Derek Fai
;
Liu, Dayiheng
;
Wei, Xiangpeng
; et al.
Favorite
|
TC[Scopus]:
1
|
Submit date:2024/07/04
Catastrophic Forgetting
Machine Translation
Monolingual And Bilingual Knowledge