Greg Diamos
Impact in
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- Parallel Computing and Optimization Techniques
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- Advanced Neural Network Applications
Papers in
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- Speech Recognition and Synthesis 3
- Natural Language Processing Techniques 2
- Machine Learning and Data Classification 1
- Adversarial Robustness in Machine Learning 1
- Topic Modeling 1
- Co-authors
- Vijay Janapa Reddi (3 shared papers)Paulius Micikevicius (1 shared paper)Guenther Schmuelling (1 shared paper)David Kanter (1 shared paper)Gu-Yeon Wei (1 shared paper)David E. Patterson (1 shared paper)Cody Coleman (1 shared paper)Peter Mattson (2 shared papers)
- Journals
- IEEE Micro (1 paper)Neural Information Processing Systems (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- ChinaUnited StatesIsrael
In The Last Decade
Greg Diamos
5 papers receiving 97 citations
Peers
Comparison fields: 5 of 25
- Hardware and Architecture 22
- Computer Vision and Pattern Recognition 42
- Artificial Intelligence 49
- Computer Networks and Communications 30
- Software 3
Countries citing papers authored by Greg Diamos
This map shows the geographic impact of Greg Diamos'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 Greg Diamos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Greg Diamos more than expected).
Fields of papers citing papers by Greg Diamos
This network shows the impact of papers produced by Greg Diamos. 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 Greg Diamos. The network helps show where Greg Diamos may publish in the future.
Co-authors
The 25 scholars most cited alongside Greg Diamos, 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 | 2020 | 91 | |
| 2 | Multilingual Spoken Words Corpus | 2021 | 7 |
| 3 | 2021 | 2 | |
| 4 | EPNAS: Efficient Progressive Neural Architecture Search | 2019 | 1 |
| 5 | HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models | 2018 | 1 |
About Greg Diamos
Greg Diamos is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Neurology and Electrical and Electronic Engineering, having authored 5 papers that have together received 102 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (3 papers), Natural Language Processing Techniques (2 papers), Machine Learning and Data Classification (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Brain Tumor Detection and Classification (1 paper), Adversarial Robustness in Machine Learning (1 paper), Music and Audio Processing (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Hardware and Architecture (22 citations), Computer Vision and Pattern Recognition (42 citations), Artificial Intelligence (49 citations), Computer Networks and Communications (30 citations) and Software (3 citations). Greg Diamos has collaborated with scholars based in China, United States and Israel. Frequent co-authors include Vijay Janapa Reddi, Paulius Micikevicius, Guenther Schmuelling, David Kanter, Gu-Yeon Wei, David E. Patterson, Cody Coleman, Peter Mattson, Christine Cheng and Hanlin Tang. Their work appears in journals such as IEEE Micro, Neural Information Processing Systems and arXiv (Cornell University).
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.