13 citations · 43 across the 9 of their papers we have counts for
19 papers
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models
Yizhou Xu, TianQi Hou, ShanSuo Liang +1
We consider the problem of reconstructing the signal and the hidden variables from observations coming from a multi-layer network with rotationally invariant weight matrices. The m…
Finite Sample Identification of Wide Shallow Neural Networks with Biases
Massimo Fornasier, Timo Klock, Marco Mondelli +1
Artificial neural networks are functions depending on a finite number of parameters typically encoded as weights and biases. The identification of the parameters of the network fro…
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
Jean Barbier, TianQi Hou, Marco Mondelli +1
We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms per…
Polar Coded Computing: The Role of the Scaling Exponent
Dorsa Fathollahi, Marco Mondelli
We consider the problem of coded distributed computing using polar codes. The average execution time of a coded computing system is related to the error probability for transmissio…
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…