16 citations · 27 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Updates on the density of states method in finite temperature symplectic gauge theories
David Mason, Ed Bennett, Biagio Lucini +4
First-order phase transitions in the early universe have rich phenomenological implications, such as the production of a potentially detectable signal of stochastic relic backgroun…
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…
Topological Data Analysis of Monopole Current Networks in Lattice Gauge Theory
Xavier Crean, Jeffrey Giansiracusa, Biagio Lucini
In -dimensional pure compact lattice gauge theory, we analyse topological aspects of the dynamics of monopoles across the deconfinement phase transition. We do this using…