5 papers
Why Adam Works Better with : The Missing Gradient Scale Invariance Principle
Alberto Fernández-Hernández, Cristian Pérez-Corral, Jose I. Mestre +2
Adam has been at the core of large-scale training for almost a decade, yet a simple empirical fact remains unaccounted for: both validation scores and the qualitative behaviour of…
GLAI: GreenLightningAI for Accelerated Training through Knowledge Decoupling
Jose I. Mestre, Alberto Fernández-Hernández, Cristian Pérez-Corral +3
In this work we introduce GreenLightningAI (GLAI), a new architectural block designed as an alternative to conventional MLPs. The central idea is to separate two types of knowledge…
Regime Change Hypothesis: Foundations for Decoupled Dynamics in Neural Network Training
Cristian Pérez-Corral, Alberto Fernández-Hernández, Jose I. Mestre +3
Despite the empirical success of DNN, their internal training dynamics remain difficult to characterize. In ReLU-based models, the activation pattern induced by a given input deter…
Sinusoidal Initialization, Time for a New Start
Alberto Fernández-Hernández, Jose I. Mestre, Manuel F. Dolz +2
Initialization plays a critical role in Deep Neural Network training, directly influencing convergence, stability, and generalization. Common approaches such as Glorot and He initi…
OUI Need to Talk About Weight Decay: A New Perspective on Overfitting Detection
Alberto Fernández-Hernández, Jose I. Mestre, Manuel F. Dolz +2
We introduce the Overfitting-Underfitting Indicator (OUI), a novel tool for monitoring the training dynamics of Deep Neural Networks (DNNs) and identifying optimal regularization h…