Macduff Hughes
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Text Readability and Simplification
- Speech Recognition and Synthesis
- Speech and dialogue systems
- Domain Adaptation and Few-Shot Learning
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- Multimodal Machine Learning Applications
Papers in
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- Topic Modeling 2
- Natural Language Processing Techniques 2
- Text Readability and Simplification 1
- Machine Learning and Data Classification 1
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- Cassava research and cyanide 1
- Co-authors
- Mike Schuster (1 shared paper)Yonghui Wu (1 shared paper)Martin Wattenberg (1 shared paper)Zhifeng Chen (1 shared paper)Maxim Krikun (1 shared paper)Melvin Johnson (1 shared paper)Jay B. Dean (1 shared paper)Greg S. Corrado (1 shared paper)
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)Socio-Environmental Systems Modeling (1 paper)
- Partner nations
- United States
In The Last Decade
Macduff Hughes
3 papers receiving 866 citations
Macduff Hughes's Hit Papers
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 815
- Computer Vision and Pattern Recognition 365
- Health Informatics 4
- Language and Linguistics 32
- General Social Sciences 9
Countries citing papers authored by Macduff Hughes
This map shows the geographic impact of Macduff Hughes'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 Macduff Hughes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Macduff Hughes more than expected).
Fields of papers citing papers by Macduff Hughes
This network shows the impact of papers produced by Macduff Hughes. 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 Macduff Hughes. The network helps show where Macduff Hughes may publish in the future.
Co-authors
The 25 scholars most cited alongside Macduff Hughes, 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 | Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation Hit paper breakdown → | 2017 | 912 |
| 2 | 2018 | 36 | |
| 3 | International research on biotechnology of cassava and its relevance to Southeast Asian economics. | 1996 | 1 |
About Macduff Hughes
Macduff Hughes is a scholar working on Artificial Intelligence, Plant Science, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 949 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Natural Language Processing Techniques (2 papers), Text Readability and Simplification (1 paper), Machine Learning and Data Classification (1 paper) and Cassava research and cyanide (1 paper). The work is most often cited by research in Artificial Intelligence (815 citations), Computer Vision and Pattern Recognition (365 citations), Health Informatics (4 citations), Language and Linguistics (32 citations) and General Social Sciences (9 citations). Macduff Hughes has collaborated with scholars based in United States. Frequent co-authors include Mike Schuster, Yonghui Wu, Martin Wattenberg, Zhifeng Chen, Maxim Krikun, Melvin Johnson, Jay B. Dean, Greg S. Corrado, Quoc V. Le and Nikhil Thorat. Their work appears in journals such as Transactions of the Association for Computational Linguistics and Socio-Environmental Systems Modeling.
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.