Charles Lovering
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Text Readability and Simplification
Papers in
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- Topic Modeling 4
- Domain Adaptation and Few-Shot Learning 2
- Advanced Text Analysis Techniques 2
- Sentiment Analysis and Opinion Mining 1
- Natural Language Processing Techniques 1
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- Time Series Analysis and Forecasting 3
- Music and Audio Processing 3
- Data Management and Algorithms 1
- Co-authors
- Ellie Pavlick (3 shared papers)Tal Linzen (1 shared paper)Gábor N. Sárközy (2 shared papers)Eamonn Keogh (1 shared paper)Elke A. Rundensteiner (2 shared papers)Hoang Anh Dau (1 shared paper)Chris C. Tanner (2 shared papers)Rik Koncel-Kedziorski (2 shared papers)
- Journals
- Proceedings of the ACM on Human-Computer Interaction (1 paper)Transactions of the Association for Computational Linguistics (1 paper)Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (1 paper)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Charles Lovering
9 papers receiving 61 citations
Peers
Comparison fields: 5 of 26
- Health Informatics 2
- Artificial Intelligence 46
- Signal Processing 15
- Computer Vision and Pattern Recognition 13
- Cultural Studies 4
Countries citing papers authored by Charles Lovering
This map shows the geographic impact of Charles Lovering'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 Charles Lovering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Lovering more than expected).
Fields of papers citing papers by Charles Lovering
This network shows the impact of papers produced by Charles Lovering. 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 Charles Lovering. The network helps show where Charles Lovering may publish in the future.
Co-authors
The 14 scholars most cited alongside Charles Lovering, 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 | Predicting Inductive Biases of Pre-Trained Models | 2021 | 22 |
| 2 | 2018 | 12 | |
| 3 | 2022 | 8 | |
| 4 | 2017 | 5 | |
| 5 | 2017 | 5 | |
| 6 | When does data augmentation help generalization in NLP | 2020 | 5 |
| 7 | 2024 | 3 | |
| 8 | 2024 | 3 | |
| 9 | 2018 | 1 |
About Charles Lovering
Charles Lovering is a scholar working on Artificial Intelligence, Signal Processing, Information Systems, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 9 papers that have together received 64 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Time Series Analysis and Forecasting (3 papers), Music and Audio Processing (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Text Analysis Techniques (2 papers), Sentiment Analysis and Opinion Mining (1 paper), Data Management and Algorithms (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (46 citations), Signal Processing (15 citations), Computer Vision and Pattern Recognition (13 citations) and Cultural Studies (4 citations). Charles Lovering has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Ellie Pavlick, Tal Linzen, Gábor N. Sárközy, Eamonn Keogh, Elke A. Rundensteiner, Hoang Anh Dau, Chris C. Tanner, Rik Koncel-Kedziorski, Viet Dac Lai and Emmanuel Agu. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Transactions of the Association for Computational Linguistics and Repository of the Academy's Library (Library of the Hungarian 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.