26 citations · 137 across the 41 of their papers we have counts for
6 papers · 1 filter
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Ramji Venkataramanan, Kevin Kögler, Marco Mondelli
We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral di…
Successive Syndrome-Check Decoding of Polar Codes
Seyyed Ali Hashemi, Marco Mondelli, John Cioffi +1
A two-part successive syndrome-check decoding of polar codes is proposed with the first part successively refining the received codeword and the second part checking its syndrome.…
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli
Understanding the properties of neural networks trained via stochastic gradient descent (SGD) is at the heart of the theory of deep learning. In this work, we take a mean-field vie…
Decoding Reed-Muller Codes with Successive Codeword Permutations
Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli +1
A novel recursive list decoding (RLD) algorithm for Reed-Muller (RM) codes based on successive permutations (SP) of the codeword is presented. A low-complexity SP scheme applied to…
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models
Marco Mondelli, Ramji Venkataramanan
We study the problem of estimating a rank- signal in the presence of rotationally invariant noise-a class of perturbations more general than Gaussian noise. Principal Component…
When Are Solutions Connected in Deep Networks?
Quynh Nguyen, Pierre Brechet, Marco Mondelli
The question of how and why the phenomenon of mode connectivity occurs in training deep neural networks has gained remarkable attention in the research community. From a theoretica…