Alane Suhr
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
- Speech and dialogue systems
- Semantic Web and Ontologies
Papers in
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- Topic Modeling 8
- Natural Language Processing Techniques 4
- Speech and dialogue systems 3
- Domain Adaptation and Few-Shot Learning 3
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- Multimodal Machine Learning Applications 7
- Advanced Image and Video Retrieval Techniques 1
- Human Pose and Action Recognition 1
- Visual Attention and Saliency Detection 1
- Co-authors
- Yoav Artzi (6 shared papers)Mike Lewis (2 shared papers)Ming‐Wei Chang (1 shared paper)Kenton Lee (1 shared paper)Peter Shaw (1 shared paper)Peter West (2 shared papers)Iris Zhang (1 shared paper)Yejin Choi (2 shared papers)
- Journals
- AI Magazine (1 paper)Transactions of the Association for Computational Linguistics (1 paper)
- Partner nations
- United StatesIsrael
In The Last Decade
Alane Suhr
10 papers receiving 205 citations
Peers
Comparison fields: 5 of 28
- Computer Vision and Pattern Recognition 118
- Artificial Intelligence 178
- General Social Sciences 3
- Information Systems 18
- Experimental and Cognitive Psychology 8
Countries citing papers authored by Alane Suhr
This map shows the geographic impact of Alane Suhr'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 Alane Suhr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alane Suhr more than expected).
Fields of papers citing papers by Alane Suhr
This network shows the impact of papers produced by Alane Suhr. 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 Alane Suhr. The network helps show where Alane Suhr may publish in the future.
Co-authors
The 25 scholars most cited alongside Alane Suhr, 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 | 2017 | 88 | |
| 2 | 2020 | 39 | |
| 3 | 2019 | 31 | |
| 4 | 2023 | 18 | |
| 5 | 2018 | 10 | |
| 6 | 2023 | 10 | |
| 7 | 2022 | 9 | |
| 8 | 2021 | 4 | |
| 9 | 2021 | 2 | |
| 10 | 2018 | 1 | |
| 11 | 2025 | 0 |
About Alane Suhr
Alane Suhr is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications, Infectious Diseases and Organic Chemistry, having authored 11 papers that have together received 212 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Multimodal Machine Learning Applications (7 papers), Natural Language Processing Techniques (4 papers), Speech and dialogue systems (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Advanced Image and Video Retrieval Techniques (1 paper), Human Pose and Action Recognition (1 paper) and Visual Attention and Saliency Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (118 citations), Artificial Intelligence (178 citations), General Social Sciences (3 citations), Information Systems (18 citations) and Experimental and Cognitive Psychology (8 citations). Alane Suhr has collaborated with scholars based in United States and Israel. Frequent co-authors include Yoav Artzi, Mike Lewis, Ming‐Wei Chang, Kenton Lee, Peter Shaw, Peter West, Iris Zhang, Yejin Choi, Julian Michael and Zhaofeng Wu. Their work appears in journals such as AI Magazine and Transactions of the Association for Computational Linguistics.
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.