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[PB-0006] A novel resistance gene for bakanae disease in rice, qBK4T identified by GWAS
Sais-Beul Lee(Sais-Beul Lee),Ju-Won Kang(Ju-Won Kang),Ji-Yoon Lee(Ji-Yoon Lee),Nkulu Rolly Kabange(Nkulu Rolly Kabange ),Hyunggon Mang(Hyunggon Mang),Yeongho Kwon(Yeongho Kwon),Gi-Un Seong(Gi-Un Seong 한국육종학회 2022 한국육종학회 공동학술발표집 Vol.2022 No.-
[PB-0007] Gene integration approach detected QTLs Associated with Bakanae disease resistance in rice
Sais-Beul Lee(Sais-Beul Lee),Hyunggon Mang(Hyunggon Mang),Ju-Won Kang(Ju-Won Kang),Ji-Yoon Lee(Ji-Yoon Lee),Nkulu Rolly Kabange(Nkulu Rolly Kabange ),Yeongho Kwon(Yeongho Kwon),Gi-Un Seong(Gi-Un Seong 한국육종학회 2022 한국육종학회 공동학술발표집 Vol.2022 No.-
A Novel Adaptive Architecture Pruning Algorithm for Madalines
Sai Ji,Ping Yang,Shuiming Zhong,Jin Wang,Jeong-Uk Kim 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.6
Nowadays, the big success of deep learning makes artificial neural network becoming a hot topic once again, and the size of neural networks’ structure is a key visual cue for structured learning. The greater network may get the study task done well, while it may increase network computation overhead easier and cost more. Hence, network construction is an important issue, as well as a difficult problem. In this paper, we proposed a novel sensitivity-based adaptive architecture pruning algorithm for Madalines. The algorithm establishes a pruning measure based on the network sensitivity to its structure variation and a minimal disturbance principle. The measure can be used to evaluate the performance loss due to its structure changes more or less. And the loss can be compensated by relearning. Thus, the new adaptive pruning mechanism is developed with measuring, pruning, and compensating. The simulation experimental results based on some benchmark data demonstrate that the pruning measure is rationality and the new algorithm is effective.