F. Tudisco

activity

20222026

most citedContractivity of neural ODEs: an eigenvalue optimization problem

1 citations · 2 across the 15 of their papers we have counts for

collaborators

Showing 2024Show all

4 papers · 1 filter

math.SP2024

The graph $\infty$ -Laplacian eigenvalue problem

Piero Deidda, Martin Burger, Mario Putti +1

We analyze various formulations of the $\infty$ -Laplacian eigenvalue problem on graphs, comparing their properties and highlighting their respective advantages and limitations. Fir…

math.NA2024★ 1 cited

Contractivity of neural ODEs: an eigenvalue optimization problem

Nicola Guglielmi, Arturo De Marinis, Anton Savostianov +1

We propose a novel methodology to solve a key eigenvalue optimization problem which arises in the contractivity analysis of neural ODEs. When looking at contractivity properties of…

cs.LG2024

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^{2}$ -Regularized Nonlinear Networks

Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia +2

We present a unified theoretical framework connecting the first property of Deep Neural Collapse (DNC1) to the emergence of implicit low-rank bias in nonlinear networks trained wit…

math.NA2024

Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex

Anton Savostianov, Francesco Tudisco, Nicola Guglielmi

Techniques based on $k$ -th order Hodge Laplacian operators $L_{k}$ are widely used to describe the topology as well as the governing dynamics of high-order systems modeled as simplic…

The graph ∞-Laplacian eigenvalue problem

Contractivity of neural ODEs: an eigenvalue optimization problem

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in L2-Regularized Nonlinear Networks

Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex

The graph $\infty$ -Laplacian eigenvalue problem

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^{2}$ -Regularized Nonlinear Networks