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Hakim Hacid

2 papers indexed

arxivcs.LGcs.CL2026-07-06

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training

Jingwei Zuo, Cong Zeng, Ilyas Chahed, Maksim Velikanov, Dhia Eddine Rhaiem, Pasquale Balsebre, et al.

The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve both model performance and sample efficiency. Meanwhile, excessive repetition introduces the risk of…

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crossrefAI2023-09-13Cited by 25

A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

Wenbin Li, Hakim Hacid, Ebtesam Almazrouei, Merouane Debbah

The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. Machine Learnin…

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