2 papers
cs.LG2026
A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm
Duy Hoang, Bastien Berret, Olivier Bruneau +1
Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This ability, commonly referred to as…
cs.LG2024
One-Step Early Stopping Strategy using Neural Tangent Kernel Theory and Rademacher Complexity
Daniel Martin Xavier, Ludovic Chamoin, Jawher Jerray +1
The early stopping strategy consists in stopping the training process of a neural network (NN) on a set of input data before training error is minimal. The advantage is that th…