5 papers
FedSQ: Optimized Weight Averaging via Fixed Gating
Cristian Pérez-Corral, Jose I. Mestre, Alberto Fernández-Hernández +3
Federated learning (FL) enables collaborative training across organizations without sharing raw data, but it is hindered by statistical heterogeneity (non-i.i.d.\ client data) and…
-GELU: Learning Gating Hardness for Controlled ReLU-ization in Deep Networks
Cristian Pérez-Corral, Alberto Fernández-Hernández, Jose I. Mestre +2
Gaussian Error Linear Unit (GELU) is a widely used smooth alternative to Rectifier Linear Unit (ReLU), yet many deployment, compression, and analysis toolchains are most naturally…
When Learning Rates Go Wrong: Early Structural Signals in PPO Actor-Critic
Alberto Fernández-Hernández, Cristian Pérez-Corral, Jose I. Mestre +3
Deep Reinforcement Learning systems are highly sensitive to the learning rate (LR), and selecting stable and performant training runs often requires extensive hyperparameter search…
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…
From domain-landmark graph learning to problem-landmark graph generation
Cristian Pérez-Corral, Antonio Garrido, Laura Sebastia
Landmarks have long played a pivotal role in automated planning, serving as crucial elements for improving the planning algorithms. The main limitation of classical landmark extrac…