Daniel Hesslow
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- Text Readability and Simplification
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Neural Networks and Reservoir Computing 2
- Topic Modeling 1
- Natural Language Processing Techniques 1
- Neural Networks and Applications 1
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- Sparse and Compressive Sensing Techniques 1
- Co-authors
- Teven Le Scao (2 shared papers)Angela Fan (1 shared paper)Ellie Pavlick (1 shared paper)Suzana Ilić (1 shared paper)Christopher Akiki (1 shared paper)Victor Sanh (1 shared paper)Iz Beltagy (1 shared paper)Stella Biderman (1 shared paper)
- Journals
- Optics Express (1 paper)Infoscience (Ecole Polytechnique Fédérale de Lausanne) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- FranceUnited StatesSouth Korea
In The Last Decade
Daniel Hesslow
4 papers receiving 182 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Health Informatics 23
- Artificial Intelligence 139
- Computational Mathematics 1
- General Social Sciences 4
- Computer Vision and Pattern Recognition 24
Countries citing papers authored by Daniel Hesslow
This map shows the geographic impact of Daniel Hesslow'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 Daniel Hesslow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Hesslow more than expected).
Fields of papers citing papers by Daniel Hesslow
This network shows the impact of papers produced by Daniel Hesslow. 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 Daniel Hesslow. The network helps show where Daniel Hesslow may publish in the future.
Co-authors
The 24 scholars most cited alongside Daniel Hesslow, 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 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model Hit paper breakdown → | 2022 | 174 |
| 2 | 2022 | 18 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 1 |
About Daniel Hesslow
Daniel Hesslow is a scholar working on Artificial Intelligence, Computational Mechanics, Acoustics and Ultrasonics, Electrical and Electronic Engineering and Infectious Diseases, having authored 4 papers that have together received 194 indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (2 papers), Advanced Memory and Neural Computing (1 paper), Topic Modeling (1 paper), Natural Language Processing Techniques (1 paper), Sparse and Compressive Sensing Techniques (1 paper), Random lasers and scattering media (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Health Informatics (23 citations), Artificial Intelligence (139 citations), Computational Mathematics (1 citation), General Social Sciences (4 citations) and Computer Vision and Pattern Recognition (24 citations). Daniel Hesslow has collaborated with scholars based in France, United States and South Korea. Frequent co-authors include Teven Le Scao, Angela Fan, Ellie Pavlick, Suzana Ilić, Christopher Akiki, Victor Sanh, Iz Beltagy, Stella Biderman, Lintang Sutawika and Jason Phang. Their work appears in journals such as Optics Express, Infoscience (Ecole Polytechnique Fédérale de Lausanne) 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.