3 papers
cs.LG2026
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
Emanuele Francazi, Francesco Pinto, Aurelien Lucchi +1
Normalization layers were introduced to stabilize and accelerate training, yet their influence is critical already at initialization, where they shape signal propagation and output…
cs.LG2026
When Bias Meets Trainability: Connecting Theories of Initialization
Alberto Bassi, Marco Baity-Jesi, Aurelien Lucchi +2
The statistical properties of deep neural networks (DNNs) at initialization play an important role to comprehend their trainability and the intrinsic architectural biases they poss…
cs.LG2025
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model
F. S. Pezzicoli, V. Ros, F. P. Landes +1
Class imbalance (CI) is a longstanding problem in machine learning, slowing down training and reducing performances. Although empirical remedies exist, it is often unclear which on…