Austin Waters
Impact in
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis
- Topic Modeling
- Natural Language Processing Techniques
- Speech and dialogue systems
- Domain Adaptation and Few-Shot Learning
- Signal Processing top 5%
- Music and Audio Processing
- Speech and Audio Processing
Papers in
-
- Speech Recognition and Synthesis 5
- Natural Language Processing Techniques 4
- Topic Modeling 3
- Machine Learning and Algorithms 2
-
- Music and Audio Processing 4
- Speech and Audio Processing 2
- Co-authors
- Yevgen Chebotar (1 shared paper)Risto Miikkulainen (2 shared papers)Pedro J. Moreno (3 shared papers)Parisa Haghani (2 shared papers)Zhongdi Qu (2 shared papers)Joseph Reisinger (1 shared paper)Raymond J. Mooney (1 shared paper)Jason Baldridge (3 shared papers)
- Journals
- AI Magazine (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Austin Waters
10 papers receiving 354 citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 315
- Signal Processing 104
- Health Informatics 6
- Computer Science Applications 24
- Computer Vision and Pattern Recognition 79
Countries citing papers authored by Austin Waters
This map shows the geographic impact of Austin Waters'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 Austin Waters with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Austin Waters more than expected).
Fields of papers citing papers by Austin Waters
This network shows the impact of papers produced by Austin Waters. 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 Austin Waters. The network helps show where Austin Waters may publish in the future.
Co-authors
The 23 scholars most cited alongside Austin Waters, 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 | 2016 | 104 | |
| 2 | 2018 | 78 | |
| 3 | 2014 | 56 | |
| 4 | Spherical Topic Models | 2010 | 49 |
| 5 | 2021 | 27 | |
| 6 | 2022 | 27 | |
| 7 | 2019 | 20 | |
| 8 | 2016 | 18 | |
| 9 | 2021 | 7 | |
| 10 | 2013 | 6 |
About Austin Waters
Austin Waters is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computer Science Applications and Information Systems, having authored 10 papers that have together received 392 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (5 papers), Natural Language Processing Techniques (4 papers), Music and Audio Processing (4 papers), Multimodal Machine Learning Applications (3 papers), Topic Modeling (3 papers), Machine Learning and Algorithms (2 papers), Online Learning and Analytics (2 papers) and Speech and Audio Processing (2 papers). The work is most often cited by research in Artificial Intelligence (315 citations), Signal Processing (104 citations), Health Informatics (6 citations), Computer Science Applications (24 citations) and Computer Vision and Pattern Recognition (79 citations). Austin Waters has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Yevgen Chebotar, Risto Miikkulainen, Pedro J. Moreno, Parisa Haghani, Zhongdi Qu, Joseph Reisinger, Raymond J. Mooney, Jason Baldridge, Rohit Prabhavalkar and Arun Narayanan. Their work appears in journals such as AI Magazine, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Proceedings of the AAAI Conference on Artificial Intelligence and International Conference on Machine Learning.
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.