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Proceedings of the National Academy of Sciences of the United States of America|February 1, 2018
Efficiency of quantum vs. classical annealing in nonconvex learning problemsCarlo Baldassi, Riccardo ZecchinaProceedings of the National Academy of Sciences of the United States of America|December 25, 2019
Shaping the learning landscape in neural networks around wide flat minimaCarlo Baldassi, Fabrizio Pittorino, Riccardo ZecchinaPhysical Review Letters|November 9, 2019
Properties of the Geometry of Solutions and Capacity of Multilayer Neural Networks with Rectified Linear Unit ActivationsCarlo Baldassi, Enrico M Malatesta, Riccardo ZecchinaProceedings of the National Academy of Sciences of the United States of America|June 22, 2007
Efficient supervised learning in networks with binary synapsesCarlo Baldassi, Alfredo Braunstein, Nicolas Brunel, et al.Plos Computational Biology|August 21, 2015
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural NetworksAlireza Alemi, Carlo Baldassi, Nicolas Brunel, et al.Physical Review. E|September 19, 2023
Typical and atypical solutions in nonconvex neural networks with discrete and continuous weightsCarlo Baldassi, Enrico M Malatesta, Gabriele Perugini, et al.Physical Review Letters|October 3, 2015
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete SynapsesCarlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, et al.Interface Focus|November 17, 2018
From statistical inference to a differential learning rule for stochastic neural networksLuca Saglietti, Federica Gerace, Alessandro Ingrosso, et al.Physical Review. E|June 15, 2016
Learning may need only a few bits of synaptic precisionCarlo Baldassi, Federica Gerace, Carlo Lucibello, et al.Physical Review Letters|January 21, 2022
Unveiling the Structure of Wide Flat Minima in Neural NetworksCarlo Baldassi, Clarissa Lauditi, Enrico M Malatesta, et al.Pageof 4