Yin-Wen Chang
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Molecular Biology
- Cognitive Neuroscience
- Co-authors
- Chih‐Jen LinCho‐Jui HsiehKai‐Wei ChangMichael CollinsSrinadh BhojanapalliChun-Sung FerngHenry TsaiHyung Won Chung
- Topics
- Natural Language Processing Techniques (7 papers)Topic Modeling (5 papers)Speech and dialogue systems (2 papers)
- Journals
- Journal of Machine Learning ResearchTransactions of the Association for Computational LinguisticsarXiv (Cornell University)
- Partner nations
- United StatesTaiwan
In The Last Decade
Yin-Wen Chang
7 papers receiving 554 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 253
- Computer Vision and Pattern Recognition 166
- Information Systems 81
- Molecular Biology 50
- Cognitive Neuroscience 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 of co-authors of Yin-Wen Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Yin-Wen Chang. A scholar is included among the top collaborators of Yin-Wen Chang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yin-Wen Chang. Yin-Wen Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Demystifying the Better Performance of Position Encoding Variants for Transformer | 4 |
| 2 | 33 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 9 | |
| 6 | Exact Decoding of Phrase-Based Translation Models through Lagrangian Relaxation | 32 |
| 7 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVMbreakdown → | 343 |
| 8 | Feature Ranking Using Linear SVM | 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) and Speech and dialogue systems (2 papers). 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.