查看“Sunwei”的源代码


{| border="0" cellpadding="1" cellspacing="1"
|-
| 
M.Sc. Student @ IIP&nbsp;Group<br/> Department of Computer Science and Technology<br/> Nanjing University

Email: weisun_@outlook.com

|-
| &nbsp;
| &nbsp;
|}

<span style="color:#3498db"><span style="font-size:larger">'''Supervisor'''</span></span>

----

*Professor Jun-Yuan Xie 

<span style="font-size:larger"><span style="color:#3498db">'''Biography'''</span></span>

----

*I&nbsp;received my B.Sc. degree in&nbsp;of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University&nbsp;without entrance examination.&nbsp; Currently I am a second&nbsp;year M.Sc. student of Department of Computer Science and Technology&nbsp;in Nanjing University&nbsp;and a member of IIP&nbsp;Group, led by professor Jun-Yuan Xie and&nbsp; Chong-Jun Wang. 

<span style="font-size:larger"><span style="color:#3498db">'''Research Interest'''</span></span>

----

I am interested in '''Machine Learning and Multi-label Learning'''.&nbsp;

*On '''Multi-label Text Classification (MLTC),''' text features can be regarded as '''detailed description of documents''' and label sets can be '''a summarization of documents'''. '''Hybrid Topics''' from text features and label sets by LDA (a method of '''topic model''') can effectively mine global label correlations and deeper&nbsp;features.&nbsp; The paper '''"An Efficient Framework for Multi-label Text Classification"'''&nbsp; is accepted in '''IJCNN 2019'''. 
*Deep learning For multi-label text classification. We utilize '''dilated convolution''' to obtain the '''semantic understanding''' of the text and design a hybrid '''attention mechansim''' for '''different labels''' (Specifically, each label should attend to most relevant textual information).&nbsp; First,ly we initialize trainable label embeddings. Then After obationing word-level information based on Bi-LSTM, we&nbsp;get semantic understanding of texts&nbsp;based on word-level information by dilated convolution. Finally,&nbsp;we design a hybrid attention for different labels based on label embeddings.&nbsp; Besides, we add '''label cooccurrence matrix into loss function '''to guide the whole network to learn and achieve good results. 

<span style="font-size:larger"><span style="color:#3498db">'''Resources'''</span></span>

----

*[http://manikvarma.org/downloads/XC/XMLRepository.html Extreme Classiﬁcation Repository]: for large-scale multi-label datasets and off-the-shelf eXtreme Multi-Label Learning (XML) solvers. 
*[http://mulan.sourceforge.net/datasets-mlc.html Mulan Multi-Label Learning Datasets]: regular/traditional multi-label learning datasets. 
*[https://github.com/XSilverBullet/Multi-label-Paper Related Works]: This page categorizes a list of works of my interest, mainly in Multi-Label Learning. 

<span style="font-size:larger"><span style="color:#3498db">'''Rewards or Honors'''</span></span>

*Second-Class Academic Scholarship, 2018-2019 
*First-Class Academic Scholarship, 2017-2018 
*Outstanding Graduate Student, 2017.06 
*CCF Excellent University Student, 2016.10 
*National Scholarship, 2015.11