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Neural networks trained with SGD learn distributions of increasing complexity


Nov 21, 2022
Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt

* Source code available at https://github.com/sgoldt/dist_inc_comp 

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Data-driven emergence of convolutional structure in neural networks


Feb 01, 2022
Alessandro Ingrosso, Sebastian Goldt

* Main text: 18 pages, 4 figures; Supplementary Material: 4 pages, 4 figures 

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Input correlations impede suppression of chaos and learning in balanced rate networks


Jan 24, 2022
Rainer Engelken, Alessandro Ingrosso, Ramin Khajeh, Sven Goedeke, L. F. Abbott


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Epidemic mitigation by statistical inference from contact tracing data


Sep 20, 2020
Antoine Baker, Indaco Biazzo, Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta, Alessandro Ingrosso, Florent Krzakala, Fabio Mazza, Marc Mézard, Anna Paola Muntoni, Maria Refinetti, Stefano Sarao Mannelli, Lenka Zdeborová

* 21 pages, 7 figures 

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Optimal Learning with Excitatory and Inhibitory synapses


May 25, 2020
Alessandro Ingrosso

* 16 pages, 5 figures 

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Training dynamically balanced excitatory-inhibitory networks


Dec 29, 2018
Alessandro Ingrosso, L. F. Abbott

* 12 pages, 7 figures 

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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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Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model


Jun 11, 2016
Furong Huang, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Ernest Fraenkel, Michael Hawrylycz, Ed Lein, Alessandro Ingrosso, Srinivas Turaga


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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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