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An Analytical Theory of Curriculum Learning in Teacher-Student Networks


Jun 15, 2021
Luca Saglietti, Stefano Sarao Mannelli, Andrew Saxe

* 10 pages + appendix 

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Probing transfer learning with a model of synthetic correlated datasets


Jun 09, 2021
Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew Saxe, Lenka Zdeborová


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Solvable Model for Inheriting the Regularization through Knowledge Distillation


Dec 02, 2020
Luca Saglietti, Lenka Zdeborová


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Large deviations for the perceptron model and consequences for active learning


Dec 09, 2019
Hugo Cui, Luca Saglietti, Lenka Zdeborová

* 25 pages, 7 figures 

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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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Gaussian Process Prior Variational Autoencoders


Oct 28, 2018
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi


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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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