J. Edward Hu
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Statistical and Nonlinear Physics
- Molecular Biology
- Co-authors
- Benjamin Van DurmeMatt PostHuda KhayrallahTongfei ChenPatrick XiaRachel RudingerAdam PoliakAaron Steven White
- Topics
- Topic Modeling (4 papers)Natural Language Processing Techniques (4 papers)Multimodal Machine Learning Applications (2 papers)
- Journals
- International Journal of Applied Earth Observation and GeoinformationarXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
J. Edward Hu
8 papers receiving 156 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 162
- Computer Vision and Pattern Recognition 50
- Information Systems 11
- Statistical and Nonlinear Physics 7
- Molecular Biology 6
Countries citing papers authored by J. Edward Hu
This map shows the geographic impact of J. Edward Hu'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 J. Edward Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Edward Hu more than expected).
Fields of papers citing papers by J. Edward Hu
This network shows the impact of papers produced by J. Edward Hu. 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 J. Edward Hu. The network helps show where J. Edward Hu may publish in the future.
Co-authorship network of co-authors of J. Edward Hu
This figure shows the co-authorship network connecting the top 25 collaborators of J. Edward Hu. A scholar is included among the top collaborators of J. Edward Hu 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 J. Edward Hu. J. Edward Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer | 6 |
| 3 | Quality enhancement of VVC intra-frame coding based on HGRDN | 1 |
| 4 | Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks | 13 |
| 5 | 62 | |
| 6 | 34 | |
| 7 | Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations | 4 |
| 8 | 49 |
About J. Edward Hu
J. Edward Hu is a scholar working on Computational Mathematics, Ecological Modeling and Artificial Intelligence, having authored 8 papers that have together received 170 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (4 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Artificial Intelligence (162 citations), Computer Vision and Pattern Recognition (50 citations) and Health Informatics (1 citation). J. Edward Hu has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Benjamin Van Durme, Matt Post, Huda Khayrallah, Tongfei Chen, Patrick Xia, Rachel Rudinger, Adam Poliak, Aaron Steven White, Ellie Pavlick and Greg Yang. Their work appears in journals such as International Journal of Applied Earth Observation and Geoinformation, arXiv (Cornell University) and Neural Information Processing Systems.
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.