3 citations · 5 across the 8 of their papers we have counts for
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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…
Stochastic weight matrix dynamics during learning and Dyson Brownian motion
Gert Aarts, Biagio Lucini, Chanju Park
We demonstrate that the update of weight matrices in learning algorithms can be described in the framework of Dyson Brownian motion, thereby inheriting many features of random matr…