Seunghak Yu
- Artificial Intelligence top 5%
- Sociology and Political Science top 10%
- Computer Vision and Pattern Recognition top 10%
- Information Systems top 10%
- Molecular Biology
- Co-authors
- Giovanni Da San MartinoPreslav NakovAlberto Barrón‐CedeñoHeriberto CuayáhuitlStefano CresciRoberto Di PietroAlexander G. SchwingTamir Hazan
- Topics
- Topic Modeling (12 papers)Multimodal Machine Learning Applications (7 papers)Speech and dialogue systems (5 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionSociology and Political Science
- Journals
- BioinformaticsNeurocomputingMethods
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Seunghak Yu
23 papers receiving 514 citations
Peers
Comparison fields: 5 of 79
- Artificial Intelligence 407
- Sociology and Political Science 205
- Computer Vision and Pattern Recognition 104
- Information Systems 85
- Molecular Biology 27
Countries citing papers authored by Seunghak Yu
This map shows the geographic impact of Seunghak Yu'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 Seunghak Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seunghak Yu more than expected).
Fields of papers citing papers by Seunghak Yu
This network shows the impact of papers produced by Seunghak Yu. 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 Seunghak Yu. The network helps show where Seunghak Yu may publish in the future.
Co-authorship network of co-authors of Seunghak Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Seunghak Yu. A scholar is included among the top collaborators of Seunghak Yu 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 Seunghak Yu. Seunghak Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 16 | |
| 4 | 94 | |
| 5 | 155 | |
| 6 | 53 | |
| 7 | 65 | |
| 8 | On-Device Neural Language Model Based Word Prediction | 8 |
| 9 | 22 | |
| 10 | 8 | |
| 11 | 13 | |
| 12 | 10 | |
| 13 | 24 | |
| 14 | 4 | |
| 15 | 3 | |
| 16 | 1 | |
| 17 | 16 | |
| 18 | 2 | |
| 19 | 2 | |
| 20 | 1 |
About Seunghak Yu
Seunghak Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Otorhinolaryngology, having authored 24 papers that have together received 543 indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Multimodal Machine Learning Applications (7 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Artificial Intelligence (407 citations), Computer Vision and Pattern Recognition (104 citations) and Sociology and Political Science (205 citations). Seunghak Yu has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Giovanni Da San Martino, Preslav Nakov, Alberto Barrón‐Cedeño, Heriberto Cuayáhuitl, Stefano Cresci, Roberto Di Pietro, Alexander G. Schwing, Tamir Hazan, Idan Schwartz and Jihie Kim. Their work appears in journals such as Bioinformatics, Neurocomputing and Methods.
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.