“Sunwei”的版本间的差异
来自南京大学IIP
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*Professor Jun-Yuan Xie | *Professor Jun-Yuan Xie | ||
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*I received my B.Sc. degree in of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University without entrance examination. Currently I am a second year M.Sc. student of Department of Computer Science and Technology in Nanjing University and a member of IIP Group, led by professor Jun-Yuan Xie and Chong-Jun Wang. | *I received my B.Sc. degree in of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University without entrance examination. Currently I am a second year M.Sc. student of Department of Computer Science and Technology in Nanjing University and a member of IIP Group, led by professor Jun-Yuan Xie and Chong-Jun Wang. | ||
| − | <span style="font-size:larger | + | <span style="font-size:larger"><span style="color:#3498db">'''Research Interest'''</span></span> |
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*I am interested in Machine Learning and Multi-label Learning. | *I am interested in Machine Learning and Multi-label Learning. | ||
| − | *Specifically, now my first work is to exploit label correlations on multi-label learning, i.e. 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 represent the whole label correlations. | + | *Specifically, now my first work is to exploit label correlations on multi-label learning, i.e. 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 represent the whole label correlations. |
| + | *My second work is to apply deep learning to 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). First, we initialize trainable label embeddings. We obation word-level information base on Bi-LSTM and get semantic understanding of a document based on word-level information. Then, we design a hybrid attention for different labels based on label embeddings. 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="font-size:larger"><span style="color:#3498db">'''Resources'''</span></span> |
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*[https://github.com/XSilverBullet/Multi-label-Paper Related Works]: This page categorizes a list of works of my interest, mainly in Multi-Label Learning. | *[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="font-size:larger"><span style="color:#3498db">'''Rewards or Honors'''</span></span> |
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2019年3月1日 (五) 16:08的版本
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M.Sc. Student @ IIP Group Email: weisun_@outlook.com | |
Supervisor
- Professor Jun-Yuan Xie
Biography
- I received my B.Sc. degree in of Soochow University in June 2017. In the same year, I was admitted to study for a Master degree in Nanjing University without entrance examination. Currently I am a second year M.Sc. student of Department of Computer Science and Technology in Nanjing University and a member of IIP Group, led by professor Jun-Yuan Xie and Chong-Jun Wang.
Research Interest
- I am interested in Machine Learning and Multi-label Learning.
- Specifically, now my first work is to exploit label correlations on multi-label learning, i.e. 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 represent the whole label correlations.
- My second work is to apply deep learning to 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). First, we initialize trainable label embeddings. We obation word-level information base on Bi-LSTM and get semantic understanding of a document based on word-level information. Then, we design a hybrid attention for different labels based on label embeddings. Besides, we add label cooccurrence matrix into loss function to guide the whole network to learn and achieve good results.
Resources
- Extreme Classification Repository: for large-scale multi-label datasets and off-the-shelf eXtreme Multi-Label Learning (XML) solvers.
- Mulan Multi-Label Learning Datasets: regular/traditional multi-label learning datasets.
- Related Works: This page categorizes a list of works of my interest, mainly in Multi-Label Learning.
Rewards or Honors
- 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
