Youssef Mroueh

1.7k total citations · 1 hit paper
27 papers, 579 citations indexed

About

Youssef Mroueh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Youssef Mroueh has authored 27 papers receiving a total of 579 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Youssef Mroueh's work include Domain Adaptation and Few-Shot Learning (5 papers), Neural Networks and Applications (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Youssef Mroueh is often cited by papers focused on Domain Adaptation and Few-Shot Learning (5 papers), Neural Networks and Applications (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Youssef Mroueh collaborates with scholars based in United States, Italy and Switzerland. Youssef Mroueh's co-authors include Payel Das, Etienne Marcheret, Vaibhava Goel, Inkit Padhi, Brian Belgodere, Jerret Ross, Vijil Chenthamarakshan, Raphaël Pestourie, Steven G. Johnson and Tom Sercu and has published in prestigious journals such as The Annals of Statistics, Physical Review X and npj Computational Materials.

In The Last Decade

Youssef Mroueh

25 papers receiving 552 citations

Hit Papers

align trajectories

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.

Large-scale chemical language representations capture molecular structure and properties

2022 227 citations Jerret Ross, Brian Belgodere et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Youssef Mroueh United States	8	173	149	131	131	119	27	579
Jiho Yoo South Korea	12	144 0.8×	101 0.7×	213 1.6×	244 1.9×	121 1.0×	20	802
Masayuki Karasuyama Japan	15	277 1.6×	64 0.4×	222 1.7×	174 1.3×	32 0.3×	58	730
Minseok Kim South Korea	8	178 1.0×	23 0.2×	56 0.4×	49 0.4×	90 0.8×	47	693
Manish Kumar India	17	172 1.0×	100 0.7×	619 4.7×	24 0.2×	99 0.8×	56	824
Takanori Fujiwara Japan	15	144 0.8×	26 0.2×	252 1.9×	33 0.3×	82 0.7×	69	640
Tao B. Schardl United States	11	150 0.9×	80 0.5×	142 1.1×	34 0.3×	16 0.1×	27	644
Xiaoqiang Wang China	14	233 1.3×	85 0.6×	89 0.7×	29 0.2×	17 0.1×	60	492
D. Wood Canada	17	354 2.0×	352 2.4×	97 0.7×	54 0.4×	129 1.1×	88	1000
Jean Barbier Italy	10	134 0.8×	32 0.2×	32 0.2×	43 0.3×	82 0.7×	32	463

Countries citing papers authored by Youssef Mroueh

Since Specialization

Citations

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

Fields of papers citing papers by Youssef Mroueh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youssef Mroueh

This figure shows the co-authorship network connecting the top 25 collaborators of Youssef Mroueh. A scholar is included among the top collaborators of Youssef Mroueh 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 Youssef Mroueh. Youssef Mroueh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ross, Jerret, Brian Belgodere, Samuel C. Hoffman, et al.. (2025). GP-MoLFormer: a foundation model for molecular generation. Digital Discovery. 4(10). 2684–2696. 3 indexed citations

2.

Belgodere, Brian, Igor Melnyk, Youssef Mroueh, et al.. (2024). Auditing and Generating Synthetic Data with Controllable Trust Trade-offs. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 1–1. 4 indexed citations

3.

Goldfeld, Ziv, et al.. (2024). Gromov–Wasserstein distances: Entropic regularization, duality and sample complexity. The Annals of Statistics. 52(4). 3 indexed citations

4.

Mroueh, Youssef, et al.. (2023). Effective Dynamics of Generative Adversarial Networks. Physical Review X. 13(4). 3 indexed citations

5.

Melnyk, Igor, Youssef Mroueh, Inkit Padhi, et al.. (2022). Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge. Journal of Artificial Intelligence Research. 73. 437–459. 25 indexed citations

6.

Ross, Jerret, Brian Belgodere, Vijil Chenthamarakshan, et al.. (2022). Large-scale chemical language representations capture molecular structure and properties. Nature Machine Intelligence. 4(12). 1256–1264. 227 indexed citations breakdown →

7.

Chuang, Ching-Yao & Youssef Mroueh. (2021). Fair Mixup: Fairness via Interpolation. arXiv (Cornell University). 2 indexed citations

8.

Mroueh, Youssef, et al.. (2021). Improved Mutual Information Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(10). 9009–9017. 5 indexed citations

9.

Zhang, Wei, Xiaodong Cui, Mingrui Liu, et al.. (2020). Improving Efficiency in Large-Scale Decentralized Distributed Training. 3022–3026. 6 indexed citations

10.

Pestourie, Raphaël, et al.. (2020). Active learning of deep surrogates for PDEs: application to metasurface design. npj Computational Materials. 6(1). 53 indexed citations

11.

Melnyk, Igor, et al.. (2019). Improved Adversarial Image Captioning. International Conference on Learning Representations. 1 indexed citations

12.

Liu, Mingrui, Youssef Mroueh, Jerret Ross, et al.. (2019). Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets. arXiv (Cornell University). 5 indexed citations

13.

Melnyk, Igor, et al.. (2018). Improved Image Captioning with Adversarial Semantic Alignment.. arXiv (Cornell University). 6 indexed citations

14.

Santos, Cícero Nogueira dos, et al.. (2018). Generative Feature Matching Networks. 3 indexed citations

15.

Mroueh, Youssef & Tom Sercu. (2017). Fisher GAN. Neural Information Processing Systems. 30. 2513–2523. 9 indexed citations

16.

Potamianos, Gerasimos, et al.. (2017). Exploring ROI size in deep learning based lipreading. University of Thessaly Institutional Repository (University of Thessaly). 15 indexed citations

17.

Mroueh, Youssef, Etienne Marcheret, & Vaibhava Goel. (2015). Deep multimodal learning for Audio-Visual Speech Recognition. 2130–2134. 149 indexed citations

18.

Ciliberto, Carlo, Youssef Mroueh, Tomaso Poggio, & Lorenzo Rosasco. (2015). Convex Learning of Multiple Tasks and their Structure. arXiv (Cornell University). 1548–1557. 23 indexed citations

19.

Mroueh, Youssef, et al.. (2012). Multiclass Learning with Simplex Coding. DSpace@MIT (Massachusetts Institute of Technology). 25. 2789–2797. 6 indexed citations

20.

Mroueh, Youssef, Tomaso Poggio, Lorenzo Rosasco, & Jean-Jacques Slotine. (2011). Multi-Class Learning: Simplex Coding And Relaxation Error. DSpace@MIT (Massachusetts Institute of Technology).

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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