Arvind Neelakantan
- Artificial Intelligence top 2%
- Topic Modeling 10
- Natural Language Processing Techniques 9
- Speech and dialogue systems 2
- Advanced Graph Neural Networks 2
- Advanced Text Analysis Techniques 1
- Domain Adaptation and Few-Shot Learning 1
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- Multimodal Machine Learning Applications 2
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- Biomedical Text Mining and Ontologies 1
- Co-authors
- Andrew McCallumAlexandre PassosBenjamin RothMing‐Wei ChangDaniel DuckworthSemih YavuzBill ByrneAmit Dubey
- Cited by
- Artificial IntelligenceManagement Science and Operations ResearchComputer Vision and Pattern Recognition
- Journals
- Journal of the Association for Information Science and Technology (1 paper)arXiv (Cornell University) (1 paper)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited KingdomAlgeria
In The Last Decade
Arvind Neelakantan
11 papers receiving 589 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 607
- Management Science and Operations Research 57
- Computer Vision and Pattern Recognition 68
- Statistics, Probability and Uncertainty 21
- Statistical and Nonlinear Physics 30
Countries citing papers authored by Arvind Neelakantan
This map shows the geographic impact of Arvind Neelakantan'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 Arvind Neelakantan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvind Neelakantan more than expected).
Fields of papers citing papers by Arvind Neelakantan
This network shows the impact of papers produced by Arvind Neelakantan. 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 Arvind Neelakantan. The network helps show where Arvind Neelakantan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Arvind Neelakantan, 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 | 2021 | 1 | |
| 2 | 2019 | 92 | |
| 3 | Towards a better understanding of Vector Quantized Autoencoders | 2018 | 7 |
| 4 | 2016 | 3 | |
| 5 | 2016 | 17 | |
| 6 | 2016 | 52 | |
| 7 | 2015 | 140 | |
| 8 | Compositional Vector Space Models for Knowledge Base Inference. | 2015 | 32 |
| 9 | 2015 | 48 | |
| 10 | 2014 | 244 | |
| 11 | Active error detection and resolution for speech-to-speech translation. | 2012 | 9 |
About Arvind Neelakantan
Arvind Neelakantan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology, having authored 11 papers that have together received 645 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (9 papers), Speech and dialogue systems (2 papers), Multimodal Machine Learning Applications (2 papers), Advanced Graph Neural Networks (2 papers), Biomedical Text Mining and Ontologies (1 paper), Advanced Text Analysis Techniques (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). The work is most often cited by research in Artificial Intelligence (607 citations), Management Science and Operations Research (57 citations) and Computer Vision and Pattern Recognition (68 citations). Arvind Neelakantan has collaborated with scholars based in United States, United Kingdom and Algeria. Frequent co-authors include Andrew McCallum, Alexandre Passos, Benjamin Roth, Ming‐Wei Chang, Daniel Duckworth, Semih Yavuz, Bill Byrne, Amit Dubey, Chinnadhurai Sankar and Ben Goodrich. Their work appears in journals such as Journal of the Association for Information Science and Technology, arXiv (Cornell University) and National Conference on Artificial Intelligence.
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.