3 papers
cs.LG2026
Study of Training Dynamics for Memory-Constrained Fine-Tuning
Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi +2
Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propo…
cs.LG2025
LaCoOT: Layer Collapse through Optimal Transport
Victor Quétu, Zhu Liao, Nour Hezbri +2
Although deep neural networks are well-known for their outstanding performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, pos…
cs.LG2024
Till the Layers Collapse: Compressing a Deep Neural Network through the Lenses of Batch Normalization Layers
Zhu Liao, Nour Hezbri, Victor Quétu +2
Today, deep neural networks are widely used since they can handle a variety of complex tasks. Their generality makes them very powerful tools in modern technology. However, deep ne…