158 citations · 1.4k across the 32 of their papers we have counts for
32 papers
Typical and atypical solutions in non-convex neural networks with discrete and continuous weights
Carlo Baldassi, Enrico M. Malatesta, Gabriele Perugini +1
We study the binary and continuous negative-margin perceptrons as simple non-convex neural network models learning random rules and associations. We analyze the geometry of the lan…
Systematically and efficiently improving -means initialization by pairwise-nearest-neighbor smoothing
Carlo Baldassi
We present a meta-method for initializing (seeding) the -means clustering algorithm called PNN-smoothing. It consists in splitting a given dataset into random subsets, clust…
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini +3
We systematize the approach to the investigation of deep neural network landscapes by basing it on the geometry of the space of implemented functions rather than the space of param…
Quantum Approximate Optimization Algorithm applied to the binary perceptron
Pietro Torta, Glen B. Mbeng, Carlo Baldassi +2
We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the opt…
Learning through atypical "phase transitions" in overparameterized neural networks
Carlo Baldassi, Clarissa Lauditi, Enrico M. Malatesta +3
Current deep neural networks are highly overparameterized (up to billions of connection weights) and nonlinear. Yet they can fit data almost perfectly through variants of gradient…
Unveiling the structure of wide flat minima in neural networks
Carlo Baldassi, Clarissa Lauditi, Enrico M. Malatesta +2
The success of deep learning has revealed the application potential of neural networks across the sciences and opened up fundamental theoretical problems. In particular, the fact t…