Iulia Turc
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Speech Recognition and Synthesis
- Text Readability and Simplification
Papers in
-
- Topic Modeling 4
- Natural Language Processing Techniques 4
- Text Readability and Simplification 1
- Explainable Artificial Intelligence (XAI) 1
-
- Multimodal Machine Learning Applications 2
- Co-authors
- Kristina Toutanova (1 shared paper)Ming‐Wei Chang (1 shared paper)Kenton Lee (2 shared papers)Jonathan H. Clark (1 shared paper)John Wieting (1 shared paper)Dan Garrette (1 shared paper)Slav Petrov (2 shared papers)Michael J. Collins (2 shared papers)
- Journals
- Computational Linguistics (2 papers)Transactions of the Association for Computational Linguistics (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Iulia Turc
5 papers receiving 156 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 141
- Health Informatics 3
- Computer Vision and Pattern Recognition 36
- Management Science and Operations Research 12
- Information Systems 21
Countries citing papers authored by Iulia Turc
This map shows the geographic impact of Iulia Turc'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 Iulia Turc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iulia Turc more than expected).
Fields of papers citing papers by Iulia Turc
This network shows the impact of papers produced by Iulia Turc. 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 Iulia Turc. The network helps show where Iulia Turc may publish in the future.
Co-authors
The 17 scholars most cited alongside Iulia Turc, 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 | Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation | 2019 | 74 |
| 2 | 2022 | 59 | |
| 3 | 2023 | 22 | |
| 4 | 2023 | 7 | |
| 5 | 2020 | 2 |
About Iulia Turc
Iulia Turc is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Education, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 164 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (4 papers), Multimodal Machine Learning Applications (2 papers), Text Readability and Simplification (1 paper), Education and Critical Thinking Development (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Artificial Intelligence (141 citations), Health Informatics (3 citations), Computer Vision and Pattern Recognition (36 citations), Management Science and Operations Research (12 citations) and Information Systems (21 citations). Iulia Turc has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Kristina Toutanova, Ming‐Wei Chang, Kenton Lee, Jonathan H. Clark, John Wieting, Dan Garrette, Slav Petrov, Michael J. Collins, Hannah Rashkin and Matthew S. Lamm. Their work appears in journals such as Computational Linguistics, 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.