20 citations · 33 across the 19 of their papers we have counts for
21 papers
Why Backdooring Neural Networks is so Easy?
Issam Seddik, Mohamed El Amine Seddik
Securing modern AI systems against backdoor attacks remains an open challenge and requires fundamentally principled estimates of the adversary's budget -- the poison fraction a…
How Does Attention Help? Insights from Random Matrices on Signal Recovery from Sequence Models
Mohamed El Amine Seddik
We study the spectral properties of sample covariance matrices constructed from pooled sequence representations, where token embeddings are drawn from a fixed two-class Gaussian mi…
-LoRA: Effective Fine-Tuning via Base Model Rescaling
Aymane El Firdoussi, El Mahdi Chayti, Mohamed El Amine Seddik +1
Fine-tuning has proven to be highly effective in adapting pre-trained models to perform better on new desired tasks with minimal data samples. Among the most widely used approaches…
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across…
On the Stability of the Jacobian Matrix in Deep Neural Networks
Benjamin Dadoun, Soufiane Hayou, Hanan Salam +2
Deep neural networks are known to suffer from exploding or vanishing gradients as depth increases, a phenomenon closely tied to the spectral behavior of the input-output Jacobian.…
High-Dimensional Analysis of Bootstrap Ensemble Classifiers
Malik Tiomoko, Hamza Cherkaoui, Mohamed El Amine Seddik +3
Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of bootstrap techniques applied to the Least S…