Yin-Wen Chang
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- Multimodal Machine Learning Applications 1
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 7
- Topic Modeling 5
- Speech and dialogue systems 2
- Bayesian Modeling and Causal Inference 1
- Machine Learning and Algorithms 1
- Text and Document Classification Technologies 1
- Information Systems top 10%
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- semigroups and automata theory 1
- Co-authors
- Chih‐Jen LinCho‐Jui HsiehKai‐Wei ChangMichael CollinsSrinadh BhojanapalliChun-Sung FerngHenry TsaiHyung Won Chung
- Journals
- Journal of Machine Learning Research (1 paper)Transactions of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesTaiwan
In The Last Decade
Yin-Wen Chang
7 papers receiving 554 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Computer Vision and Pattern Recognition 166
- Artificial Intelligence 253
- Computational Mathematics 4
- Information Systems 81
- Signal Processing 34
Countries citing papers authored by Yin-Wen Chang
This map shows the geographic impact of Yin-Wen Chang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yin-Wen Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yin-Wen Chang more than expected).
Fields of papers citing papers by Yin-Wen Chang
This network shows the impact of papers produced by Yin-Wen Chang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yin-Wen Chang. The network helps show where Yin-Wen Chang may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Yin-Wen Chang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Demystifying the Better Performance of Position Encoding Variants for Transformer | 2021 | 4 |
| 2 | 2021 | 33 | |
| 3 | 2017 | 0 | |
| 4 | 2014 | 7 | |
| 5 | 2013 | 9 | |
| 6 | Exact Decoding of Phrase-Based Translation Models through Lagrangian Relaxation | 2011 | 32 |
| 7 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVMbreakdown → | 2010 | 343 |
| 8 | Feature Ranking Using Linear SVM | 2008 | 153 |
About Yin-Wen Chang
Yin-Wen Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 8 papers that have together received 581 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (7 papers), Topic Modeling (5 papers), Speech and dialogue systems (2 papers), Multimodal Machine Learning Applications (1 paper), Bayesian Modeling and Causal Inference (1 paper), semigroups and automata theory (1 paper), Machine Learning and Algorithms (1 paper) and Text and Document Classification Technologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (166 citations), Artificial Intelligence (253 citations) and Computational Mathematics (4 citations). Yin-Wen Chang has collaborated with scholars based in United States and Taiwan. Frequent co-authors include Chih‐Jen Lin, Cho‐Jui Hsieh, Kai‐Wei Chang, Michael Collins, Srinadh Bhojanapalli, Chun-Sung Ferng, Henry Tsai, Hyung Won Chung, Alexander M. Rush and Michael J. Collins. Their work appears in journals such as Journal of Machine Learning Research, Transactions of the Association for Computational Linguistics and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.