Stephen Mussmann
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning and Algorithms
- Advanced Text Analysis Techniques
- Machine Learning and Data Classification
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Machine Learning and Data Classification 4
- Machine Learning and Algorithms 4
- Topic Modeling 4
- Natural Language Processing Techniques 3
- Speech and dialogue systems 2
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- Complex Network Analysis Techniques 2
- Opinion Dynamics and Social Influence 2
- Co-authors
- Percy Liang (5 shared papers)Arun Tejasvi Chaganty (1 shared paper)Stefano Ermon (1 shared paper)Robin Jia (1 shared paper)Jennifer Neville (2 shared papers)Christopher Yeh (1 shared paper)Joseph J. Pfeiffer (2 shared papers)Baharan Mirzasoleiman (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United States
In The Last Decade
Stephen Mussmann
9 papers receiving 85 citations
Peers
Comparison fields: 5 of 22
- Artificial Intelligence 84
- Computer Vision and Pattern Recognition 24
- General Social Sciences 3
- Structural Biology 1
- Information Systems 10
Countries citing papers authored by Stephen Mussmann
This map shows the geographic impact of Stephen Mussmann'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 Stephen Mussmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Mussmann more than expected).
Fields of papers citing papers by Stephen Mussmann
This network shows the impact of papers produced by Stephen Mussmann. 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 Stephen Mussmann. The network helps show where Stephen Mussmann may publish in the future.
Co-authors
The 23 scholars most cited alongside Stephen Mussmann, 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 | 2018 | 50 | |
| 2 | Learning and inference via maximum inner product search | 2016 | 13 |
| 3 | 2020 | 10 | |
| 4 | Selection via Proxy: Efficient Data Selection for Deep Learning | 2020 | 6 |
| 5 | 2015 | 4 | |
| 6 | 2018 | 4 | |
| 7 | On the Relationship between Data Efficiency and Error for Uncertainty Sampling | 2018 | 2 |
| 8 | 2023 | 2 | |
| 9 | 2024 | 2 | |
| 10 | 2014 | 0 |
About Stephen Mussmann
Stephen Mussmann is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Condensed Matter Physics, Information Systems and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 93 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Machine Learning and Algorithms (4 papers), Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Complex Network Analysis Techniques (2 papers), Opinion Dynamics and Social Influence (2 papers), Speech and dialogue systems (2 papers) and Mental Health Research Topics (1 paper). The work is most often cited by research in Artificial Intelligence (84 citations), Computer Vision and Pattern Recognition (24 citations), General Social Sciences (3 citations), Structural Biology (1 citation) and Information Systems (10 citations). Stephen Mussmann has collaborated with scholars based in United States. Frequent co-authors include Percy Liang, Arun Tejasvi Chaganty, Stefano Ermon, Robin Jia, Jennifer Neville, Christopher Yeh, Joseph J. Pfeiffer, Baharan Mirzasoleiman, Cody Coleman and Jure Leskovec. Their work appears in journals such as Proceedings of the VLDB Endowment, International Conference on Learning Representations, arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and International Conference on Machine Learning.
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.