Amir Gholami

3.9k total citations · 1 hit paper
23 papers, 1.0k citations indexed

About

Amir Gholami is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Amir Gholami has authored 23 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Amir Gholami's work include Advanced Neural Network Applications (13 papers), Stochastic Gradient Optimization Techniques (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Amir Gholami is often cited by papers focused on Advanced Neural Network Applications (13 papers), Stochastic Gradient Optimization Techniques (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Amir Gholami collaborates with scholars based in United States, China and South Korea. Amir Gholami's 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 and has published in prestigious journals such as Journal of Mathematical Biology, arXiv (Cornell University) and eScholarship (California Digital Library).

In The Last Decade

Amir Gholami

22 papers receiving 1.0k citations

Hit Papers

Q-BERT: Hessian Based Ultra Low Precision Quantization of... 2020 2026 2022 2024 2020 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
    2020 223 citations Sheng Shen, Zhen Dong et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Amir Gholami United States 11 657 591 156 53 48 23 1.0k
Shan You China 18 723 1.1× 613 1.0× 55 0.4× 43 0.8× 57 1.2× 47 1.1k
Levent Sagun United States 9 433 0.7× 382 0.6× 62 0.4× 23 0.4× 38 0.8× 18 867
Zhifei Zhang United States 10 882 1.3× 498 0.8× 91 0.6× 84 1.6× 69 1.4× 20 1.4k
Steven R. Young United States 13 400 0.6× 161 0.3× 204 1.3× 53 1.0× 43 0.9× 36 741
Rohan Varma United States 8 575 0.9× 201 0.3× 73 0.5× 174 3.3× 39 0.8× 20 872
Tianlong Chen United States 17 533 0.8× 651 1.1× 44 0.3× 37 0.7× 60 1.3× 73 1.1k
Mohsen Machhout Tunisia 18 616 0.9× 671 1.1× 241 1.5× 146 2.8× 150 3.1× 187 1.3k
S. Srinivas Kumar India 16 149 0.2× 623 1.1× 70 0.4× 61 1.2× 73 1.5× 77 919

Countries citing papers authored by Amir Gholami

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Amir Gholami

This figure shows the co-authorship network connecting the top 25 collaborators of Amir Gholami. A scholar is included among the top collaborators of Amir Gholami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Amir Gholami. Amir Gholami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hooper, Coleman, Muthucumaru Maheswaran, Joonki Paik, et al.. (2025). Squeezed Attention: Accelerating Long Context Length LLM Inference. 32631–32652.
2.
Lee, Nick, Sehoon Kim, Karttikeya Mangalam, et al.. (2024). LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement. 6498–6526. 7 indexed citations
3.
Baldi, Tommaso Lisini, J. Ngadiuba, Nhan Viet Tran, et al.. (2024). Reliable edge machine learning hardware for scientific applications. eScholarship (California Digital Library). 1–5. 1 indexed citations
4.
Lee, Nick, Se Hoon Kim, Coleman Hooper, et al.. (2024). TinyAgent: Function Calling at the Edge. 80–88. 4 indexed citations
5.
Kim, Sehoon, Amir Gholami, Zhewei Yao, et al.. (2022). Integer-Only Zero-Shot Quantization for Efficient Speech Recognition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 4288–4292. 8 indexed citations
6.
Kim, Sehoon, Sheng Shen, David Thorsley, et al.. (2022). Learned Token Pruning for Transformers. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 784–794. 49 indexed citations
7.
Yu, Shixing, Zhewei Yao, Amir Gholami, et al.. (2022). Hessian-Aware Pruning and Optimal Neural Implant. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3665–3676. 22 indexed citations
8.
Yao, Zhewei, Amir Gholami, Sheng Shen, et al.. (2021). ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 35(12). 10665–10673. 88 indexed citations
9.
Cai, Yaohui, Zhewei Yao, Zhen Dong, et al.. (2020). ZeroQ: A Novel Zero Shot Quantization Framework. 13166–13175. 195 indexed citations
10.
Yao, Zhewei, Amir Gholami, Kurt Keutzer, & Michael W. Mahoney. (2020). PyHessian: Neural Networks Through the Lens of the Hessian. 581–590. 65 indexed citations
11.
Ma, Linjian, Jiayu Ye, Zhewei Yao, et al.. (2020). Inefficiency of K-FAC for Large Batch Size Training. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 5053–5060. 8 indexed citations
12.
Shen, Sheng, Zhen Dong, Jiayu Ye, et al.. (2020). Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 8815–8821. 223 indexed citations breakdown →
13.
Subramanian, Shashank, Amir Gholami, & George Biros. (2019). Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect. Journal of Mathematical Biology. 79(3). 941–967. 28 indexed citations
14.
Dong, Zhen, Zhewei Yao, Amir Gholami, Michael W. Mahoney, & Kurt Keutzer. (2019). HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision. 293–302. 224 indexed citations
15.
Yao, Zhewei, Amir Gholami, Qi Lei, Kurt Keutzer, & Michael W. Mahoney. (2018). Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Neural Information Processing Systems. 31. 4949–4959. 8 indexed citations
16.
Gholami, Amir, Ariful Azad, Peter Jin, Kurt Keutzer, & Aydın Buluç. (2018). Integrated Model, Batch, and Domain Parallelism in Training Neural Networks. eScholarship (California Digital Library). 77–86. 40 indexed citations
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19.
Gholami, Amir, Ariful Azad, Kurt Keutzer, & Aydın Buluç. (2017). Integrated Model and Data Parallelism in Training Neural Networks.. arXiv (Cornell University). 3 indexed citations
20.
Gholami, Amir, Dhairya Malhotra, Hari Sundar, & George Biros. (2014). FFT, FMM, or MULTIGRID? A comparative study of state-of-the-art poisson solvers.. 2 indexed citations

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.

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