Behrooz Ghorbani
Impact in
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- Stochastic Gradient Optimization Techniques
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
Papers in
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- Bayesian Methods and Mixture Models 2
- Neural Networks and Applications 1
- Topic Modeling 1
- Speech Recognition and Synthesis 1
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- Image and Signal Denoising Methods 2
- Face and Expression Recognition 1
- Co-authors
- Shankar Krishnan (2 shared papers)Ying Xiao (1 shared paper)Theodor Misiakiewicz (2 shared papers)Andrea Montanari (3 shared papers)Mei Song (1 shared paper)Patrick Fernandes (1 shared paper)Seyed Mohammad Kazem Aghamir (1 shared paper)Ali Pasha Meysamie (1 shared paper)
- Journals
- The Annals of Statistics (1 paper)PubMed (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesPortugalIran
In The Last Decade
Behrooz Ghorbani
7 papers receiving 66 citations
Peers
Comparison fields: 5 of 27
- Computational Mathematics 1
- Artificial Intelligence 48
- Statistics and Probability 9
- Statistical and Nonlinear Physics 11
- Computer Vision and Pattern Recognition 18
Countries citing papers authored by Behrooz Ghorbani
This map shows the geographic impact of Behrooz Ghorbani'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 Behrooz Ghorbani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Behrooz Ghorbani more than expected).
Fields of papers citing papers by Behrooz Ghorbani
This network shows the impact of papers produced by Behrooz Ghorbani. 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 Behrooz Ghorbani. The network helps show where Behrooz Ghorbani may publish in the future.
Co-authors
The 14 scholars most cited alongside Behrooz Ghorbani, 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 | 2019 | 31 | |
| 2 | Limitations of Lazy Training of Two-layers Neural Network | 2019 | 16 |
| 3 | Is antibiotic prophylaxis necessary in patients undergoing ureterolithotripsy? | 2011 | 11 |
| 4 | 2023 | 5 | |
| 5 | 2020 | 4 | |
| 6 | An Instability in Variational Inference for Topic Models | 2019 | 1 |
| 7 | The Effect of Network Depth on the Optimization Landscape | 2019 | 1 |
| 8 | 2023 | 0 |
About Behrooz Ghorbani
Behrooz Ghorbani is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Pediatrics, Perinatology and Child Health and Pulmonary and Respiratory Medicine, having authored 8 papers that have together received 69 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (2 papers), Bayesian Methods and Mixture Models (2 papers), Computational and Text Analysis Methods (1 paper), Neural Networks and Applications (1 paper), Pediatric Urology and Nephrology Studies (1 paper), Topic Modeling (1 paper), Speech Recognition and Synthesis (1 paper) and Face and Expression Recognition (1 paper). The work is most often cited by research in Computational Mathematics (1 citation), Artificial Intelligence (48 citations), Statistics and Probability (9 citations), Statistical and Nonlinear Physics (11 citations) and Computer Vision and Pattern Recognition (18 citations). Behrooz Ghorbani has collaborated with scholars based in United States, Portugal and Iran. Frequent co-authors include Shankar Krishnan, Ying Xiao, Theodor Misiakiewicz, Andrea Montanari, Mei Song, Patrick Fernandes, Seyed Mohammad Kazem Aghamir, Ali Pasha Meysamie, Alborz Salavati and Markus Freitag. Their work appears in journals such as The Annals of Statistics, PubMed, arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.
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.