Andrew M. Dai
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Management Science and Operations Research top 10%
- Signal Processing top 10%
- Co-authors
- Quoc V. LeJakob UszkoreitIllia PolosukhinJacob DevlinTom KwiatkowskiMichael CollinsKristina ToutanovaChris Alberti
- Topics
- Topic Modeling (7 papers)Natural Language Processing Techniques (5 papers)Machine Learning in Healthcare (2 papers)
- Journals
- Transactions of the Association for Computational LinguisticsarXiv (Cornell University)Meeting of the Association for Computational Linguistics
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Andrew M. Dai
10 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 1.2k
- Computer Vision and Pattern Recognition 442
- Information Systems 185
- Management Science and Operations Research 56
- Signal Processing 54
Countries citing papers authored by Andrew M. Dai
This map shows the geographic impact of Andrew M. Dai'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 Andrew M. Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew M. Dai more than expected).
Fields of papers citing papers by Andrew M. Dai
This network shows the impact of papers produced by Andrew M. Dai. 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 Andrew M. Dai. The network helps show where Andrew M. Dai may publish in the future.
Co-authorship network of co-authors of Andrew M. Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew M. Dai. A scholar is included among the top collaborators of Andrew M. Dai 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 Andrew M. Dai. Andrew M. Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Improved Patient Classification with Language Model Pretraining Over Clinical Notes. | 2 |
| 3 | Natural Questions: A Benchmark for Question Answering Researchbreakdown → | 984 |
| 4 | 77 | |
| 5 | Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records. | 9 |
| 6 | An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation | 21 |
| 7 | 201 | |
| 8 | Language-independent compound splitting with morphological operations | 33 |
| 9 | Author Disambiguation: A Nonparametric Topic andCo-authorship Model | 5 |
| 10 | Proceedings of NIPS Workshop on Applications for Topic Models Text and Beyond | 21 |
About Andrew M. Dai
Andrew M. Dai is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cultural Studies, having authored 10 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (5 papers) and Machine Learning in Healthcare (2 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computer Vision and Pattern Recognition (442 citations) and Health Informatics (15 citations). Andrew M. Dai has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Quoc V. Le, Jakob Uszkoreit, Illia Polosukhin, Jacob Devlin, Tom Kwiatkowski, Michael Collins, Kristina Toutanova, Chris Alberti, Slav Petrov and Ankur P. Parikh. Their work appears in journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Meeting of the Association for Computational Linguistics.
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.