Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Sia-Huat Tan"'
Autor:
Wei-Li Yang, Qi Li, Jing Sun, Sia Huat Tan, Yan-Hong Tang, Miao-Miao Zhao, Yu-Yang Li, Xi Cao, Jin-Cun Zhao, Jin-Kui Yang
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 2442-2454 (2022)
Cathepsin L (CTSL), a cysteine protease that can cleave and activate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, could be a promising therapeutic target for coronavirus disease 2019 (COVID-19). However, there is st
Externí odkaz:
https://doaj.org/article/43b61e7f3143492f9fcea1f4b0c8a5a3
Autor:
Yanzhi Wang, Zhezhi He, Sia Huat Tan, Sheng Lin, Deliang Fan, Xiaolong Ma, Linfeng Zhang, Zhengang Li, Geng Yuan, Kaisheng Ma, Xuehai Qian, Xue Lin, Shaokai Ye
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:4930-4944
Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compressi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d95cd5040fd7292955474189e3d86a9b
https://doi.org/10.1109/tnnls.2021.3063265
https://doi.org/10.1109/tnnls.2021.3063265
Knowledge distillation conducts an effective model compression method while holding some limitations:(1) the feature based distillation methods only focus on distilling the feature map but are lack of transferring the relation of data examples; (2) t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0cb728cc5f7e04b078428d734613f2c
Autor:
Wei-Li Yang, Qi Li, Jing Sun, Sia Huat Tan, Yan-Hong Tang, Miao-Miao Zhao, Yu-Yang Li, Xi Cao, Jin-Cun Zhao, Jin-Kui Yang
Publikováno v:
Computational and structural biotechnology journal. 20
Cathepsin L (CTSL), a cysteine protease that can cleave and activate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, could be a promising therapeutic target for coronavirus disease 2019 (COVID-19). However, there is st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d1d11eb25a4fab35822399895540db9
https://pubmed.ncbi.nlm.nih.gov/35602976
https://pubmed.ncbi.nlm.nih.gov/35602976
Publikováno v:
CICC
Deep neural networks present a promising future in applications, ranging from face ID on mobile phones to self-driving cars. Weight pruning and quantization act as valuable solutions to release the burden of computation and memory. Figure 1 shows the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7031984a0acbbf4ddb8e5926782413b
https://doi.org/10.1109/cicc51472.2021.9431519
https://doi.org/10.1109/cicc51472.2021.9431519
Autor:
Shaokai Ye, Yuanqing Miao, Kaisheng Ma, Zhanhong Tan, Yifu Wu, Hongyang Chen, Dehui Li, Sia-Huat Tan, Jiebo Song, Xiaolong Ma, Yanzhi Wang
Publikováno v:
DAC
Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07ef3ee1fd2b16da132cac5d2c83865c
http://arxiv.org/abs/2002.04997
http://arxiv.org/abs/2002.04997
Autor:
Shaokai Ye, Kailu Wu, Mu Zhou, Jiebo Song, Yunfei Yang, Chenglong Bao, Kaidi Xu, Sia Huat Tan, Kaisheng Ma
Publikováno v:
CVPR
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade off between the source do
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5aa9eedeb9ab4e20eb341396badd192e
http://arxiv.org/abs/1911.12796
http://arxiv.org/abs/1911.12796
Autor:
Zhanhong Tan, Jiebo Song, Xiaolong Ma, Sia-Huat Tan, Hongyang Chen, Yuanqing Miao, Yifu Wu, Shaokai Ye, Yanzhi Wang, Dehui Li, Kaisheng Ma
Publikováno v:
DAC: Annual ACM/IEEE Design Automation Conference; 2020, Issue 57, p1130-1135, 6p