3 citations · 5 across the 23 of their papers we have counts for
7 papers · 1 filter
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Aymane El Firdoussi, Mohamed El Amine Seddik, Soufiane Hayou +3
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A pot…
Falcon Mamba: The First Competitive Attention-free 7B Language Model
Jingwei Zuo, Maksim Velikanov, Dhia Eddine Rhaiem +4
In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with…
Alignment with Preference Optimization Is All You Need for LLM Safety
Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi +3
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achi…
Falcon2-11B Technical Report
Quentin Malartic, Nilabhra Roy Chowdhury, Ruxandra Cojocaru +14
We introduce Falcon2-11B, a foundation model trained on over five trillion tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a vision-to-text model. We report our f…
ViSpeR: Multilingual Audio-Visual Speech Recognition
Sanath Narayan, Yasser Abdelaziz Dahou Djilali, Ankit Singh +2
This work presents an extensive and detailed study on Audio-Visual Speech Recognition (AVSR) for five widely spoken languages: Chinese, Spanish, English, Arabic, and French. We hav…
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
Jingwei Zuo, George Arvanitakis, Mthandazo Ndhlovu +1
Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques. While companies have successfully integrated HAR in…
Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling
Jingwei Zuo, Hakim Hacid
Human Activity Recognition (HAR) has been studied for decades, from data collection, learning models, to post-processing and result interpretations. However, the inherent hierarchy…