Amir Gholami
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- Advanced Neural Network Applications 13
- Artificial Intelligence top 2%
- Stochastic Gradient Optimization Techniques 5
- Domain Adaptation and Few-Shot Learning 5
- Adversarial Robustness in Machine Learning 3
- Natural Language Processing Techniques 3
- Machine Learning and ELM 2
- Computational Mathematics top 10%
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques 3
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- CCD and CMOS Imaging Sensors 2
- Journals
- Journal of Mathematical Biology (1 paper)arXiv (Cornell University) (1 paper)eScholarship (California Digital Library) (2 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Amir Gholami
22 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Computer Vision and Pattern Recognition 591
- Artificial Intelligence 657
- Computational Mathematics 11
- Hardware and Architecture 44
- Neurology 38
Countries citing papers authored by Amir Gholami
This map shows the geographic impact of Amir Gholami'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 Amir Gholami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Gholami more than expected).
Fields of papers citing papers by Amir Gholami
This network shows the impact of papers produced by Amir Gholami. 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 Amir Gholami. The network helps show where Amir Gholami may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Amir Gholami, 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 | 2025 | 0 | |
| 2 | 2024 | 7 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 4 | |
| 5 | 2022 | 8 | |
| 6 | 2022 | 49 | |
| 7 | 2022 | 22 | |
| 8 | 2021 | 88 | |
| 9 | 2020 | 195 | |
| 10 | 2020 | 65 | |
| 11 | 2020 | 8 | |
| 12 | Q-BERT: Hessian Based Ultra Low Precision Quantization of BERTbreakdown → | 2020 | 223 |
| 13 | 2019 | 28 | |
| 14 | 2019 | 224 | |
| 15 | Hessian-based Analysis of Large Batch Training and Robustness to Adversaries | 2018 | 8 |
| 16 | 2018 | 40 | |
| 17 | 2018 | 18 | |
| 18 | 2018 | 6 | |
| 19 | Integrated Model and Data Parallelism in Training Neural Networks. | 2017 | 3 |
| 20 | FFT, FMM, or MULTIGRID? A comparative study of state-of-the-art poisson solvers. | 2014 | 2 |
About Amir Gholami
Amir Gholami is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture, having authored 23 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (13 papers), Stochastic Gradient Optimization Techniques (5 papers), Domain Adaptation and Few-Shot Learning (5 papers), Adversarial Robustness in Machine Learning (3 papers), Parallel Computing and Optimization Techniques (3 papers), Natural Language Processing Techniques (3 papers), CCD and CMOS Imaging Sensors (2 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (591 citations), Artificial Intelligence (657 citations) and Computational Mathematics (11 citations). Amir Gholami has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Kurt Keutzer, Michael W. Mahoney, Zhewei Yao, Zhen Dong, Sheng Shen, Yaohui Cai, Jiayu Ye, Linjian Ma, Zhen Dong and Mustafa Mustafa. Their work appears in journals such as Journal of Mathematical Biology, arXiv (Cornell University) and eScholarship (California Digital Library).
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.