Issam Laradji

2.0k total citations · 1 hit paper
24 papers, 594 citations indexed

About

Issam Laradji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Issam Laradji has authored 24 papers receiving a total of 594 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 5 papers in Computational Mechanics. Recurrent topics in Issam Laradji's work include Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Multimodal Machine Learning Applications (4 papers). Issam Laradji is often cited by papers focused on Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Multimodal Machine Learning Applications (4 papers). Issam Laradji collaborates with scholars based in Canada, United States and Saudi Arabia. Issam Laradji's co-authors include Lahouari Ghouti, Mohammad Alshayeb, Pau Rodríguez, David Vázquez, Simon Lacoste-Julien, Mark Schmidt, Pedro O. Pinheiro, Dzmitry Bahdanau, Negar Rostamzadeh and M. Caccia and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Information and Software Technology.

In The Last Decade

Issam Laradji

20 papers receiving 573 citations

Hit Papers

Software defect prediction using ensemble learning on sel... 2014 2026 2018 2022 2014 50 100 150 200 250
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. Software defect prediction using ensemble learning on selected features
    2014 288 citations Issam Laradji, Mohammad Alshayeb et al. Information and Software Technology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Issam Laradji Canada 11 259 236 231 163 98 24 594
Yudong Liu China 11 58 0.2× 146 0.6× 23 0.1× 123 0.8× 97 1.0× 29 427
Ha-Jin Yu South Korea 15 76 0.3× 697 3.0× 29 0.1× 184 1.1× 55 0.6× 59 957
Iyad Tumar Palestinian Territory 8 97 0.4× 88 0.4× 52 0.2× 39 0.2× 89 0.9× 25 311
Gen Zhang China 5 33 0.1× 210 0.9× 49 0.2× 130 0.8× 27 0.3× 14 460
Kuo-Ping Wu Taiwan 8 220 0.8× 130 0.6× 243 1.1× 48 0.3× 363 3.7× 16 674
Egor Bondarev Netherlands 10 58 0.2× 154 0.7× 71 0.3× 155 1.0× 127 1.3× 69 399
Ayaz Isazadeh Iran 12 162 0.6× 204 0.9× 67 0.3× 28 0.2× 108 1.1× 46 384
Mohammed Eshtay Jordan 9 72 0.3× 262 1.1× 47 0.2× 57 0.3× 68 0.7× 15 394
Nemanja Petrović United States 11 55 0.2× 85 0.4× 47 0.2× 217 1.3× 19 0.2× 24 486
Dejun Mu China 10 176 0.7× 229 1.0× 34 0.1× 51 0.3× 65 0.7× 42 404

Countries citing papers authored by Issam Laradji

Since Specialization
Citations

This map shows the geographic impact of Issam Laradji'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 Issam Laradji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Issam Laradji more than expected).

Fields of papers citing papers by Issam Laradji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Issam Laradji. 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 Issam Laradji. The network helps show where Issam Laradji may publish in the future.

Co-authorship network of co-authors of Issam Laradji

This figure shows the co-authorship network connecting the top 25 collaborators of Issam Laradji. A scholar is included among the top collaborators of Issam Laradji 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 Issam Laradji. Issam Laradji 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
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Rodríguez, J.A., Issam Laradji, Pau Rodríguez, et al.. (2025). StarVector: Generating Scalable Vector Graphics Code from Images and Text. PolyPublie (École Polytechnique de Montréal). 16175–16186. 1 indexed citations
3.
Rodríguez, J.A., Issam Laradji, Sai Rajeswar, et al.. (2025). StarVector: Generating Scalable Vector Graphics Code from Images and Text. Proceedings of the AAAI Conference on Artificial Intelligence. 39(28). 29691–29693. 2 indexed citations
4.
5.
Laradji, Issam. (2024). Where are the Masks: Instance Segmentation with Image-level Supervision. arXiv (Cornell University). 255.
6.
Calixto, Iacer, et al.. (2023). LLM aided semi-supervision for efficient Extractive Dialog Summarization. Pure Amsterdam UMC. 10002–10009. 3 indexed citations
7.
Vechtomova, Olga, et al.. (2023). PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation. 5316–5327. 3 indexed citations
8.
Lomonaco, Vincenzo, Lorenzo Pellegrini, Pau Rodríguez, et al.. (2022). CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions. CINECA IRIS Institutial research information system (University of Pisa). 23 indexed citations
9.
Rodríguez, Pau, et al.. (2022). Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. 47–57. 32 indexed citations
10.
Laradji, Issam, et al.. (2022). A Survey of Self-Supervised and Few-Shot Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(4). 1–20. 61 indexed citations
11.
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Laradji, Issam, et al.. (2022). Neural Point Light Fields. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18398–18408. 27 indexed citations
13.
Laradji, Issam, Alzayat Saleh, Pau Rodríguez, et al.. (2021). Weakly supervised underwater fish segmentation using affinity LCFCN. Scientific Reports. 11(1). 17379–17379. 24 indexed citations
14.
Rodríguez, Pau, M. Caccia, Alexandre Lacoste, et al.. (2021). Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 1036–1045. 19 indexed citations
15.
Laradji, Issam, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, & Mark Schmidt. (2020). Proposal-Based Instance Segmentation With Point Supervision. 2126–2130. 31 indexed citations
16.
Caccia, M., Pau Rodríguez, Min Lin, et al.. (2020). Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. Neural Information Processing Systems. 33. 16532–16545. 23 indexed citations
17.
Nutini, Julie, et al.. (2016). Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph. arXiv (Cornell University). 547–556. 3 indexed citations
18.
Nutini, Julie, et al.. (2015). Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. 1632–1641. 20 indexed citations
19.
Laradji, Issam, Mohammad Alshayeb, & Lahouari Ghouti. (2014). Software defect prediction using ensemble learning on selected features. Information and Software Technology. 58. 388–402. 288 indexed citations breakdown →
20.
Laradji, Issam, et al.. (2014). XML classification using ensemble learning on extracted features. 42. 1–6.

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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