Pete Warden
Impact in
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- Music and Audio Processing
- Speech and Audio Processing
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- Advanced Neural Network Applications
- Context-Aware Activity Recognition Systems
Papers in
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- Speech Recognition and Synthesis 2
- Topic Modeling 1
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- IoT and Edge/Fog Computing 2
- Co-authors
- Nicholas D. Lane (1 shared paper)Vijay Janapa Reddi (6 shared papers)Colby Banbury (4 shared papers)Brian Plancher (3 shared papers)Matthew Stewart (3 shared papers)Sachin Katti (2 shared papers)Yiping Kang (1 shared paper)Peter Mattson (1 shared paper)
- Journals
- Communications of the ACM (2 papers)Computer (1 paper)Nature Machine Intelligence (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesChinaIndia
In The Last Decade
Pete Warden
10 papers receiving 254 citations
Peers
Comparison fields: 5 of 71
- Signal Processing 38
- Computer Vision and Pattern Recognition 69
- Artificial Intelligence 101
- Computer Networks and Communications 63
- Hardware and Architecture 11
Countries citing papers authored by Pete Warden
This map shows the geographic impact of Pete Warden'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 Pete Warden with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pete Warden more than expected).
Fields of papers citing papers by Pete Warden
This network shows the impact of papers produced by Pete Warden. 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 Pete Warden. The network helps show where Pete Warden may publish in the future.
Co-authors
The 15 scholars most cited alongside Pete Warden, 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 | TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers | 2019 | 169 |
| 2 | 2021 | 26 | |
| 3 | 2018 | 22 | |
| 4 | Big Data Glossary | 2011 | 16 |
| 5 | 2023 | 15 | |
| 6 | 2023 | 10 | |
| 7 | Multilingual Spoken Words Corpus | 2021 | 7 |
| 8 | 2025 | 2 | |
| 9 | 2023 | 2 | |
| 10 | Privacy-Preserving Inference on the Edge: Mitigating a New Threat Model | 2020 | 1 |
About Pete Warden
Pete Warden is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Sociology and Political Science, having authored 10 papers that have together received 270 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (2 papers), Context-Aware Activity Recognition Systems (2 papers), Speech Recognition and Synthesis (2 papers), Green IT and Sustainability (1 paper), Embedded Systems Design Techniques (1 paper), Digital Transformation in Industry (1 paper), Topic Modeling (1 paper) and Parallel Computing and Optimization Techniques (1 paper). The work is most often cited by research in Signal Processing (38 citations), Computer Vision and Pattern Recognition (69 citations), Artificial Intelligence (101 citations), Computer Networks and Communications (63 citations) and Hardware and Architecture (11 citations). Pete Warden has collaborated with scholars based in United States, China and India. Frequent co-authors include Nicholas D. Lane, Vijay Janapa Reddi, Colby Banbury, Brian Plancher, Matthew Stewart, Sachin Katti, Yiping Kang, Peter Mattson, David Kanter and Greg Diamos. Their work appears in journals such as Communications of the ACM, Computer, Nature Machine Intelligence, arXiv (Cornell University) and Neural Information Processing Systems.
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.