14 citations · 40 across the 28 of their papers we have counts for
8 papers · 1 filter
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments
Mert Unsal, Ali Maatouk, Antonio De Domenico +2
As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments. The size of deep learning models makes it difficult to de…
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Ali Maatouk, Fadhel Ayed, Nicola Piovesan +3
We introduce TeleQnA, the first benchmark dataset designed to evaluate the knowledge of Large Language Models (LLMs) in telecommunications. Comprising 10,000 questions and answers,…
Large Language Models for Telecom: Forthcoming Impact on the Industry
Ali Maatouk, Nicola Piovesan, Fadhel Ayed +2
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing field…
An Optimization Framework For Anomaly Detection Scores Refinement With Side Information
Ali Maatouk, Fadhel Ayed, Wenjie Li +3
This paper considers an anomaly detection problem in which a detection algorithm assigns anomaly scores to multi-dimensional data points, such as cellular networks' Key Performance…
Data pruning and neural scaling laws: fundamental limitations of score-based algorithms
Fadhel Ayed, Soufiane Hayou
Data pruning algorithms are commonly used to reduce the memory and computational cost of the optimization process. Recent empirical results reveal that random data pruning remains…
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
Francois Caron, Fadhel Ayed, Paul Jung +3
We consider gradient-based optimisation of wide, shallow neural networks, where the output of each hidden node is scaled by a positive parameter. The scaling parameters are non-ide…