1 citations · 1 across the 3 of their papers we have counts for
4 papers
Phase diagram and eigenvalue dynamics of stochastic gradient descent in multilayer neural networks
Chanju Park, Biagio Lucini, Gert Aarts
Hyperparameter tuning is one of the essential steps to guarantee the convergence of machine learning models. We argue that intuition about the optimal choice of hyperparameters for…
Exploring Generative Networks for Manifolds with Non-Trivial Topology
Shiyang Chen, Gert Aarts, Biagio Lucini
The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations…
Random Matrix Theory for Stochastic Gradient Descent
Chanju Park, Matteo Favoni, Biagio Lucini +1
Investigating the dynamics of learning in machine learning algorithms is of paramount importance for understanding how and why an approach may be successful. The tools of physics a…
Dyson Brownian motion and random matrix dynamics of weight matrices during learning
Gert Aarts, Ouraman Hajizadeh, Biagio Lucini +1
During training, weight matrices in machine learning architectures are updated using stochastic gradient descent or variations thereof. In this contribution we employ concepts of r…