From outage probability to ALOHA MAC layer performance analysis in distributed WSNs. - Mohamed El Amine Seddik; Viktor Toldov; Laurent Clavier; Nathalie Mitton (WCNC, 2018)

Kernel Random Matrices of Large Concentrated Data: the Example of GAN-Generated Images. - Mohamed El Amine Seddik; Mohamed Tamaazousti; Romain Couillet (ICASSP, 2019)

A Kernel Random Matrix-Based Approach for Sparse PCA. - Mohamed El Amine Seddik; Mohamed Tamaazousti; Romain Couillet (ICLR (Poster), 2019)

Deep Multi-class Adversarial Specularity Removal. - John Lin; Mohamed El Amine Seddik; Mohamed Tamaazousti; Youssef Tamaazousti; Adrien Bartoli (SCIA, 2019)

Lightweight Neural Networks From PCA & LDA Based Distilled Dense Neural Networks. - Mohamed El Amine Seddik; Hassane Essafi; Abdallah Benzine; Mohamed Tamaazousti (ICIP, 2020)

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. - Mohamed El Amine Seddik; Cosme Louart; Mohamed Tamaazousti; Romain Couillet (ICML, 2020)

Generative collaborative networks for single image super-resolution. - Mohamed El Amine Seddik; Mohamed Tamaazousti; John Lin (Neurocomputing, 2020)

Learning More Universal Representations for Transfer-Learning. - Youssef Tamaazousti; Hervé Le Borgne; Céline Hudelot; Mohamed El Amine Seddik; Mohamed Tamaazousti (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Random Matrix Theory for AI: From Theory to Practice. (Théorie des matrices aléatoires pour l'IA: De la théorie à la pratique). - Mohamed El Amine Seddik (2020)

The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. - Mohamed El Amine Seddik; Cosme Louart; Romain Couillet; Mohamed Tamaazousti (AISTATS, 2021)

Optimization-Based Neural Networks Compression. - Younes Tahiri; Mohamed El Amine Seddik; Mohamed Tamaazousti (ICIP, 2021)

Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identification. - Abdallah Benzine; Mohamed El Amine Seddik; Julien Desmarais (CoRR, 2021)

Neural Networks Classify through the Class-Wise Means of Their Representations. - Mohamed El Amine Seddik; Mohamed Tamaazousti (AAAI, 2022)

Node Feature Kernels Increase Graph Convolutional Network Robustness. - Mohamed El Amine Seddik; Changmin Wu; Johannes F. Lutzeyer; Michalis Vazirgiannis (AISTATS, 2022)

Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm. - Malik Tiomoko; Ekkehard Schnoor; Mohamed El Amine Seddik; Igor Colin; Aladin Virmaux (ICML, 2022)

Soil Moisture Estimation Using Sentinel-1/-2 Imagery Coupled With CycleGAN for Time-Series Gap Filing. - Natalia Efremova; Mohamed El Amine Seddik; Esra Erten (IEEE Trans. Geosci. Remote. Sens., 2022)

On the Accuracy of Hotelling-Type Asymmetric Tensor Deflation: A Random Tensor Analysis. - Mohamed El Amine Seddik; Maxime Guillaud; Alexis Decurninge; José Henrique de Morais Goulart (CAMSAP, 2023)

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. - Mohamed El Amine Seddik; Malik Tiomoko; Alexis Decurninge; Maxim Panov; Maxime Guillaud (UAI, 2023)

Hotelling Deflation on Large Symmetric Spiked Tensors. - Mohamed El Amine Seddik; José Henrique de Morais Goulart; Maxime Guillaud (CoRR, 2023)

A Nested Matrix-Tensor Model for Noisy Multi-view Clustering. - Mohamed El Amine Seddik; Mastane Achab; Henrique Goulart; Mérouane Debbah (CoRR, 2023)

Do Vision and Language Encoders Represent the World Similarly? - Mayug Maniparambil; Raiymbek Akshulakov; Yasser Abdelaziz Dahou Djilali; Mohamed El Amine Seddik; Sanath Narayan; Karttikeya Mangalam; Noel E. O'Connor (CVPR, 2024)

Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model. - Hugo Lebeau; Mohamed El Amine Seddik; José Henrique de Morais Goulart (ICLR, 2024)

Investigating Regularization of Self-Play Language Models. - Réda Alami; Abdalgader Abubaker; Mastane Achab; Mohamed El Amine Seddik; Salem Lahlou (CoRR, 2024)

How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse. - Mohamed El Amine Seddik; Suei-Wen Chen; Soufiane Hayou; Pierre Youssef; Mérouane Debbah (CoRR, 2024)

High-dimensional Learning with Noisy Labels. - Aymane El Firdoussi; Mohamed El Amine Seddik (CoRR, 2024)

Falcon2-11B Technical Report. - Quentin Malartic; Nilabhra Roy Chowdhury; Ruxandra Cojocaru; Mugariya Farooq; Giulia Campesan; Yasser Abdelaziz Dahou Djilali; Sanath Narayan; Ankit Singh; Maksim Velikanov; Basma El Amel Boussaha; Mohammed Al-Yafeai; Hamza Alobeidli; et al. (CoRR, 2024)

Alignment with Preference Optimization Is All You Need for LLM Safety. - Réda Alami; Ali Khalifa Almansoori; Ahmed Alzubaidi; Mohamed El Amine Seddik; Mugariya Farooq; Hakim Hacid (CoRR, 2024)

Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory. - Aymane El Firdoussi; Mohamed El Amine Seddik; Soufiane Hayou; Réda Alami; Ahmed Alzubaidi; Hakim Hacid (ICLR, 2025)

Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets. - Adam Younsi; Abdalgader Abubaker; Mohamed El Amine Seddik; Hakim Hacid; Salem Lahlou (CoRR, 2025)

High-Dimensional Analysis of Bootstrap Ensemble Classifiers. - Hamza Cherkaoui; Malik Tiomoko; Mohamed El Amine Seddik; Cosme Louart; Ekkehard Schnoor; Balázs Kégl (CoRR, 2025)

On the Stability of the Jacobian Matrix in Deep Neural Networks. - Benjamin Dadoun; Soufiane Hayou; Hanan Salam; Mohamed El Amine Seddik; Pierre Youssef (CoRR, 2025)

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance. - Jingwei Zuo; Maksim Velikanov; Ilyas Chahed; Younes Belkada; Dhia Eddine Rhayem; Guillaume Kunsch; Hakim Hacid; Hamza Yous; Brahim Farhat; Ibrahim Khadraoui; Mugariya Farooq; Giulia Campesan; et al. (CoRR, 2025)