William Yang Wang

328 papers receiving 8.1k citations

William Yang Wang's Hit Papers

Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation 2019 · 309 citations
3090+2+4Years since publication100200300

Peers

William Yang Wang
Comparison fields: 5 of 211
  • Linguistics and Language 860
  • Experimental and Cognitive Psychology 1.9k
  • Artificial Intelligence 3.1k
  • Language and Linguistics 769
  • Cognitive Neuroscience 1.4k
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Countries citing papers authored by William Yang Wang

Since Specialization
Citations

This map shows the geographic impact of William Yang Wang'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 William Yang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Yang Wang more than expected).

Fields of papers citing papers by William Yang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Yang Wang. 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 William Yang Wang. The network helps show where William Yang Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside William Yang Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with William Yang Wang Line = papers co-authored together William Yang Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 352 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012329
2
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Hit paper breakdown →
2019309
3 2019274
4 1989270
5 1973231
6 1969229
7 2020188
8 2018181
9 2015180
10 2001175
11 2018159
12 1975140
13 2011130
14 2018126
15 1979124
16 2010116
17 2019111
18 1977100
19 2007100
20 197796

About William Yang Wang

William Yang Wang is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Cultural Studies, having authored 352 papers that have together received 8.8k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (54 papers), Topic Modeling (49 papers), Multimodal Machine Learning Applications (34 papers), Phonetics and Phonology Research (32 papers), Language and cultural evolution (30 papers), Speech and dialogue systems (28 papers), Speech Recognition and Synthesis (23 papers) and Linguistic Variation and Morphology (23 papers). The work is most often cited by research in Linguistics and Language (860 citations), Experimental and Cognitive Psychology (1.9k citations), Artificial Intelligence (3.1k citations), Language and Linguistics (769 citations) and Cognitive Neuroscience (1.4k citations). William Yang Wang has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include James W. Minett, Ovid J. L. Tzeng, Gang Peng, Liang‐Shih Fan, Diyi Yang, Liang Zeng, Elizabeth Belding, Siwei Luo, Xin Wang and Matthew Y. Chen. Their work appears in journals such as Language, The Journal of the Acoustical Society of America, Industrial & Engineering Chemistry Research, Therapeutic Innovation & Regulatory Science and Proceedings of the National Academy of Sciences.

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.

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