SPARSITY

作品数：142被引量：233H指数：7
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相关机构：中国石油大学(北京)中国科学院上海交通大学中国石油更多>>
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SWG:an architecture for sparse weight gradient computation被引量：1
《Science China(Information Sciences)》2024年第2期298-317,共20页Weiwei WU Fengbin TU Xiangyu LI Shaojun WEI Shouyi YIN 
supported in part by National Natural Science Foundation of China(Grant Nos.U19B2041,62125403,92164301);National Key Research and Development Program(Grant No.2021ZD0114400);Science and Technology Innovation 2030–New Generation of AI Project(Grant No.2022ZD0115201);Beijing National Research Center for Information Science and Technology;Beijing Advanced Innovation Center for Integrated Circuits.
On-device training for deep neural networks(DNN)has become a trend due to various user preferences and scenarios.The DNN training process consists of three phases,feedforward(FF),backpropagation(BP),and weight gradien...
关键词：CNN TRAINING gradient computation SPARSITY ARCHITECTURE 
Compressed sensing application in interferometric synthetic aperture radar被引量：5
《Science China(Information Sciences)》2017年第10期183-199,共17页Liechen Li Daojing LI Zhouhao PAN 
supported by National Natural Science Foundation of China(Grant No.61271422)
A novel interferometric synthetic aperture radar （InSAR） signal processing method based on compressed sensing （CS） theory is investigated in this paper. InSAR image formation provides the scene refiectivity estima...
关键词：synthetic aperture radar （SAR） interferometric synthetic aperture radar （InSAR） compressed sensing （CS） sparse sampling sparsity in the transform domain 
Joint sparsity and fidelity regularization for segmentation-driven CT image preprocessing被引量：1
《Science China(Information Sciences)》2016年第3期142-148,共7页Feng LIU Huibin LI 
supported by the National Natural Science Foundation of China(Grant No.11401464);the China Postdoctoral Science Foundation(Grant No.2014M560785)
In this paper, we propose a novel segmentation-driven computed tomography （CT） image prepro- cessing approach. The proposed approach, namely, joint sparsity and fidelity regularization （JSFR） model can be regarded...
关键词：CT image HOMOGENIZATION enhancement tissue segmentation gradient fidelity gradient sparsity 
Robust sparse principal component analysis被引量：5
《Science China(Information Sciences)》2014年第9期171-184,共14页ZHAO Qian MENG DeYu XU ZongBen 
supported by National Basic Research Program of China(973)(Grant No.2013CB329404);National Natural Science Foundation of China(Grant Nos.61373114,11131006)
The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximize...
关键词：noise OUTLIER principal component analysis ROBUSTNESS SPARSITY 
Feature-aware regularization for sparse online learning被引量：2
《Science China(Information Sciences)》2014年第5期61-81,共21页OIWA Hidekazu MATSUSHIMA Shin NAKAGAWA Hiroshi 
supported by JSPS KAKENHI,Grant-in-Aid for JSPS Fellows for Hidekazu Oiwa
Learning a compact predictive model in an online setting has recently gained a great deal of at- tention. The combination of online learning with sparsity-inducing regularization enables faster learning with a smaller...
关键词：online learning supervised learning sparsity-inducing regularization feature selection sentimentanalysis 
The essential ability of sparse reconstruction of different compressive sensing strategies被引量：7
《Science China(Information Sciences)》2012年第11期2582-2589,共8页ZHANG Hai LIANG Yong GOU HaiLiang XU ZongBen 
supported by National Basic Research Program of China (Grant No. 2007CB311002);National Natural Science Foundation of China (Grant Nos. 60975036,11171272);Macao Science and Technology Development Fund (Grant No. 021/2008/A) of Macao Special Administrative Region of the People’s Republic of China
We show the essential ability of sparse signal reconstruction of different compressive sensing strate- gies,which include the L1 regularization, the L0 regularization（thresholding iteration algorithm and OMP algo- ri...
关键词：compressive sensing REGULARIZATION SPARSITY L1/2 regularization 
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