Mohammad Pezeshki
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
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- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
Papers in
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- Advanced Text Analysis Techniques 1
- Neural Networks and Applications 1
- Machine Learning and Data Classification 1
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- Advanced Image and Video Retrieval Techniques 1
- Image and Signal Denoising Methods 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Yoshua Bengio (1 shared paper)Philémon Brakel (1 shared paper)Aaron Courville (1 shared paper)Linxi Fan (1 shared paper)Soroush Mehri (1 shared paper)Farid Rashidi Mehrabadi (1 shared paper)Shahram Khadivi (1 shared paper)Mohammad Mehdi Homayounpour (1 shared paper)
- Journals
- arXiv (Cornell University) (2 papers)
- Partner nations
- IranCanadaUnited States
In The Last Decade
Mohammad Pezeshki
3 papers receiving 49 citations
Peers
Comparison fields: 5 of 33
- Computer Vision and Pattern Recognition 17
- Artificial Intelligence 25
- Media Technology 5
- Marketing 5
- Signal Processing 4
Countries citing papers authored by Mohammad Pezeshki
This map shows the geographic impact of Mohammad Pezeshki'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 Mohammad Pezeshki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Pezeshki more than expected).
Fields of papers citing papers by Mohammad Pezeshki
This network shows the impact of papers produced by Mohammad Pezeshki. 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 Mohammad Pezeshki. The network helps show where Mohammad Pezeshki may publish in the future.
Co-authors
The 8 scholars most cited alongside Mohammad Pezeshki, 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 | 2015 | 46 | |
| 2 | 2014 | 5 | |
| 3 | Deep Belief Networks for Image Denoising. | 2013 | 1 |
About Mohammad Pezeshki
Mohammad Pezeshki is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Marketing and Infectious Diseases, having authored 3 papers that have together received 52 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (1 paper), Image and Signal Denoising Methods (1 paper), Advanced Text Analysis Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Neural Networks and Applications (1 paper), Data Mining Algorithms and Applications (1 paper), Customer churn and segmentation (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (17 citations), Artificial Intelligence (25 citations), Media Technology (5 citations), Marketing (5 citations) and Signal Processing (4 citations). Mohammad Pezeshki has collaborated with scholars based in Iran, Canada and United States. Frequent co-authors include Yoshua Bengio, Philémon Brakel, Aaron Courville, Linxi Fan, Soroush Mehri, Farid Rashidi Mehrabadi, Shahram Khadivi and Mohammad Mehdi Homayounpour. Their work appears in journals such as 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.