The Latest Research In Training Modern Machine Learning Models: ‘a Deterministic Modification Of Gradient Descent That Avoids Saddle Points ” Mathematics# ” Cambridge Core Blog

This post by Lorenz Kuger reflects on the recent success of machine learning models and the associated challenges. To prevent these problems, Kuger introduces a recently published article that addresses how to modify gradient descent to avoid saddle points, which until now, has been a less researched direction. This new research is published and available now in European Journal of Applied Mathematics (EJAM). The paper introduces a deterministic gradient-based approach to avoid saddle points, which is crucial to the training process of neural networks.