research
∙
02/10/2023
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization
"Forward-only" algorithms, which train neural networks while avoiding a ...
share
research
∙
10/12/2022
Rigorous dynamical mean field theory for stochastic gradient descent methods
We prove closed-form equations for the exact high-dimensional asymptotic...
research
∙
03/22/2022
Learning curves for the multi-class teacher-student perceptron
One of the most classical results in high-dimensional learning theory pr...
research
∙
12/20/2021
The effective noise of Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is the workhorse algorithm of deep lea...
research
∙
03/08/2021
Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem
In this paper we investigate how gradient-based algorithms such as gradi...
research
∙
06/10/2020
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification
We analyze in a closed form the learning dynamics of stochastic gradient...
research
∙
02/26/2020