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Reconstruction of Pairwise Interactions using Energy-Based Models


Dec 11, 2020
Christoph Feinauer, Carlo Lucibello


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Entropic gradient descent algorithms and wide flat minima


Jun 14, 2020
Fabrizio Pittorino, Carlo Lucibello, Christoph Feinauer, Enrico M. Malatesta, Gabriele Perugini, Carlo Baldassi, Matteo Negri, Elizaveta Demyanenko, Riccardo Zecchina

* 24 pages (main text: 8 pages) 

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Clustering of solutions in the symmetric binary perceptron


Nov 18, 2019
Carlo Baldassi, Riccardo Della Vecchia, Carlo Lucibello, Riccardo Zecchina


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Generalized Approximate Survey Propagation for High-Dimensional Estimation


May 13, 2019
Luca Saglietti, Yue M. Lu, Carlo Lucibello

* ICML 2019 

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Signal propagation in continuous approximations of binary neural networks


Feb 01, 2019
George Stamatescu, Federica Gerace, Carlo Lucibello, Ian Fuss, Langford B. White


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On the role of synaptic stochasticity in training low-precision neural networks


Mar 20, 2018
Carlo Baldassi, Federica Gerace, Hilbert J. Kappen, Carlo Lucibello, Luca Saglietti, Enzo Tartaglione, Riccardo Zecchina

* Phys. Rev. Lett. 120, 268103 (2018) 
* 7 pages + 14 pages of supplementary material 

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Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes


Oct 06, 2016
Carlo Baldassi, Christian Borgs, Jennifer Chayes, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina

* Proc. Natl. Acad. Sci. U.S.A. 113(48):E7655-E7662, 2016 
* 31 pages (14 main text, 18 appendix), 12 figures (6 main text, 6 appendix) 

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Learning may need only a few bits of synaptic precision


May 27, 2016
Carlo Baldassi, Federica Gerace, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina

* Phys. Rev. E 93, 052313 (2016) 
* 38 pages (main text: 16 pages), 5 figures; http://link.aps.org/doi/10.1103/PhysRevE.93.052313 

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Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems


Feb 25, 2016
Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina

* J. Stat. Mech. 2016 (2) 023301 
* 46 pages (main text: 22), 7 figures. This is an author-created, un-copyedited version of an article published in Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/1742-5468/2016/02/023301 

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Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses


Sep 18, 2015
Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina

* Physical Review Letters, 15, 128101 (2015) url=http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.115.128101 
* 11 pages, 4 figures (main text: 5 pages, 3 figures; Supplemental Material: 6 pages, 1 figure) 

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