3 papers
cs.CL2026
When Do Concepts Become Functionally Sufficient During Language-Model Training?
Raphael Bernas, Paul G. Chevalier, Fanny Jourdan +1
Understanding a model and its learning mechanisms in depth requires identifying when its internal structures become useful, rather than simply looking at the final state. We study…
cs.CL2026
Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics
Raphael Bernas, Fanny Jourdan, Antonin Poché +1
Since their introduction, Transformer architectures have dominated Natural Language Processing (NLP). However, recent research has highlighted an inherent anisotropy phenomenon in…
cs.LG2024
Recommendation of data-free class-incremental learning algorithms by simulating future data
Eva Feillet, Adrian Popescu, Céline Hudelot
Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples fr…