Mahmoud Assran
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Computational Mechanics
- Co-authors
- Michael RabbatNicolas BallasIshan MisraPiotr BojanowskiP. VincentYann LeCunQuentin DuvalNicolas Loizou
- Topics
- Stochastic Gradient Optimization Techniques (2 papers)Distributed Control Multi-Agent Systems (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)
- Journals
- Proceedings of the IEEE2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)
In The Last Decade
Mahmoud Assran
6 papers receiving 258 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 153
- Computer Vision and Pattern Recognition 89
- Computer Networks and Communications 49
- Electrical and Electronic Engineering 32
- Computational Mechanics 22
Countries citing papers authored by Mahmoud Assran
This map shows the geographic impact of Mahmoud Assran'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 Mahmoud Assran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahmoud Assran more than expected).
Fields of papers citing papers by Mahmoud Assran
This network shows the impact of papers produced by Mahmoud Assran. 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 Mahmoud Assran. The network helps show where Mahmoud Assran may publish in the future.
Co-authorship network of co-authors of Mahmoud Assran
This figure shows the co-authorship network connecting the top 25 collaborators of Mahmoud Assran. A scholar is included among the top collaborators of Mahmoud Assran 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 Mahmoud Assran. Mahmoud Assran is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturebreakdown → | 117 |
| 2 | 54 | |
| 3 | 40 | |
| 4 | Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning | 2 |
| 5 | 46 | |
| 6 | 4 |
About Mahmoud Assran
Mahmoud Assran is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 6 papers that have together received 263 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (2 papers), Distributed Control Multi-Agent Systems (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Artificial Intelligence (153 citations), Computer Vision and Pattern Recognition (89 citations) and Health Informatics (3 citations). Mahmoud Assran has collaborated with scholars based in Canada, Israel and Sweden. Frequent co-authors include Michael Rabbat, Nicolas Ballas, Ishan Misra, Piotr Bojanowski, P. Vincent, Yann LeCun, Quentin Duval, Nicolas Loizou, Mathilde Caron and Armand Joulin. Their work appears in journals such as Proceedings of the IEEE, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and 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.