Greg Diamos
Impact in
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
-
- Advanced Neural Network Applications
Papers in
-
- Speech Recognition and Synthesis 2
- Machine Learning and Data Classification 1
- Adversarial Robustness in Machine Learning 1
- Topic Modeling 1
- Natural Language Processing Techniques 1
-
- Speech and Audio Processing 1
- Music and Audio Processing 1
- Co-authors
- Vijay Janapa Reddi (2 shared papers)Peter R. Mattson (2 shared papers)David E. Patterson (1 shared paper)Carole-Jean Wu (1 shared paper)Guenther Schmuelling (1 shared paper)Christine Cheng (1 shared paper)Gu-Yeon Wei (1 shared paper)Paulius Micikevicius (1 shared paper)
- Journals
- IEEE Micro (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Greg Diamos
5 papers receiving 454 citations
Greg Diamos's Hit Papers
Peers
Comparison fields: 5 of 51
- Hardware and Architecture 111
- Computer Vision and Pattern Recognition 180
- Computational Mathematics 5
- Computer Networks and Communications 158
- Artificial Intelligence 175
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 20 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 | MLPerf Inference Benchmark Hit paper breakdown → | 2020 | 346 |
| 2 | 2020 | 110 | |
| 3 | End to end speech recognition in English and Mandarin | 2016 | 10 |
| 4 | Multilingual Spoken Words Corpus | 2021 | 7 |
| 5 | HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models | 2018 | 1 |
| 6 | 2023 | 0 |
About Greg Diamos
Greg Diamos is a scholar working on Artificial Intelligence, Signal Processing, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 6 papers that have together received 474 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (2 papers), Machine Learning and Data Classification (1 paper), Adversarial Robustness in Machine Learning (1 paper), Speech and Audio Processing (1 paper), Music and Audio Processing (1 paper), Topic Modeling (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Hardware and Architecture (111 citations), Computer Vision and Pattern Recognition (180 citations), Computational Mathematics (5 citations), Computer Networks and Communications (158 citations) and Artificial Intelligence (175 citations). Greg Diamos has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Vijay Janapa Reddi, Peter R. Mattson, David E. Patterson, Carole-Jean Wu, Guenther Schmuelling, Christine Cheng, Gu-Yeon Wei, Paulius Micikevicius, David Kanter and Cody Coleman. Their work appears in journals such as IEEE Micro 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.