158 citations · 1.4k across the 32 of their papers we have counts for
3 papers · 1 filter
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…