Joshua Ainslie
- Artificial Intelligence top 2%
- Natural Language Processing Techniques 12
- Topic Modeling 11
- Speech Recognition and Synthesis 2
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Algorithms 1
- Health Informatics top 10%
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- Handwritten Text Recognition Techniques 2
- Image Processing and 3D Reconstruction 1
- Signal Processing top 10%
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- Machine Learning in Bioinformatics 2
- Co-authors
- Santiago OntañónIlya EcksteinAnirudh RavulaYury ZemlyanskiyMichiel de JongJames Lee-ThorpVaclav CvicekDavid Uthus
- Journals
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2 papers)BMJ (1 paper)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (1 paper)
- Partner nations
- United States
In The Last Decade
Joshua Ainslie
15 papers receiving 742 citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 497
- Health Informatics 17
- Computer Vision and Pattern Recognition 212
- Signal Processing 58
- Computational Mathematics 3
Countries citing papers authored by Joshua Ainslie
This map shows the geographic impact of Joshua Ainslie'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 Joshua Ainslie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joshua Ainslie more than expected).
Fields of papers citing papers by Joshua Ainslie
This network shows the impact of papers produced by Joshua Ainslie. 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 Joshua Ainslie. The network helps show where Joshua Ainslie may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Joshua Ainslie, 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 | GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpointsbreakdown → | 2023 | 124 |
| 2 | 2023 | 5 | |
| 3 | 2023 | 18 | |
| 4 | 2023 | 5 | |
| 5 | 2023 | 3 | |
| 6 | 2022 | 4 | |
| 7 | FNet: Mixing Tokens with Fourier Transformsbreakdown → | 2022 | 264 |
| 8 | 2022 | 2 | |
| 9 | 2022 | 113 | |
| 10 | 2022 | 21 | |
| 11 | 2021 | 54 | |
| 12 | 2021 | 7 | |
| 13 | 2021 | 6 | |
| 14 | 2020 | 148 | |
| 15 | 1989 | 2 |
About Joshua Ainslie
Joshua Ainslie is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 15 papers that have together received 776 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (12 papers), Topic Modeling (11 papers), Machine Learning in Bioinformatics (2 papers), Handwritten Text Recognition Techniques (2 papers), Speech Recognition and Synthesis (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Algorithms (1 paper) and Image Processing and 3D Reconstruction (1 paper). The work is most often cited by research in Artificial Intelligence (497 citations), Health Informatics (17 citations) and Computer Vision and Pattern Recognition (212 citations). Joshua Ainslie has collaborated with scholars based in United States. Frequent co-authors include Santiago Ontañón, Ilya Eckstein, Anirudh Ravula, Yury Zemlyanskiy, Michiel de Jong, James Lee-Thorp, Vaclav Cvicek, David Uthus, Mandy Guo and Yun-Hsuan Sung. Their work appears in journals such as Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), BMJ and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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.