Alessandro Ingrosso

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Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityVerified email at unibocconi.it
Carlo BaldassiBocconi University; ELLIS scholarVerified email at unibocconi.it
Luca SagliettiÉcole Politechnique Fédérale de Lausanne - EPFLVerified email at epfl.ch
Carlo LucibelloAssistant Professor, Bocconi UniversityVerified email at unibocconi.it
Alfredo BraunsteinDISAT, Politecnico di TorinoVerified email at polito.it
Jennifer ChayesProfessor, UC BerkeleyVerified email at jenniferchayes.com
Christian BorgsProfessor, UC BerkeleyVerified email at christianborgs.com
Luca Dall'AstaPolitecnico di TorinoVerified email at polito.it
Anna Paola MuntoniAssistant Professor, Politecnico di TorinoVerified email at polito.it
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorVerified email at columbia.edu
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Mario DipoppaUniversity of California, Los Angeles (UCLA)Verified email at g.ucla.edu
Maria RefinettiEcole Normale Supérieure de ParisVerified email at ens.fr
Sebastian GoldtInternational School of Advanced Studies (SISSA), Trieste, ItalyVerified email at sissa.it
Fabrizio AltarelliCapital Fund Management, 23 rue de l'Université, Paris, FranceVerified email at polito.it
Marc MezardBocconi University MilanoVerified email at unibocconi.it
Giovanni CataniaUniversidad Complutense MadridVerified email at ucm.es
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Indaco BiazzoÉcole Polytechnique Fédérale de LausanneVerified email at epfl.ch
Stefano SARAO MANNELLIGatsby & SWC | University College LondonVerified email at ucl.ac.uk

Alessandro Ingrosso

Senior Postdoctoral Scientist, The Abdus Salam International Centre for Theoretical Physics

Verified email at ictp.it - Homepage

Theoretical Neuroscience Machine Learning Statistical Mechanics


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Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes C Baldassi, C Borgs, JT Chayes, A Ingrosso, C Lucibello, L Saglietti, ... Proceedings of the National Academy of Sciences 113 (48), E7655-E7662, 2016	180	2016
Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Physical review letters 115 (12), 128101, 2015	142	2015
A disinhibitory circuit for contextual modulation in primary visual cortex AJ Keller, M Dipoppa, MM Roth, MS Caudill, A Ingrosso, KD Miller, ... Neuron 108 (6), 1181-1193. e8, 2020	88	2020
Local entropy as a measure for sampling solutions in constraint satisfaction problems C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Journal of Statistical Mechanics: Theory and Experiment 2016 (2), 023301, 2016	58	2016
The patient-zero problem with noisy observations F Altarelli, A Braunstein, L Dall’Asta, A Ingrosso, R Zecchina Journal of Statistical Mechanics: Theory and Experiment 2014 (10), P10016, 2014	46	2014
Training dynamically balanced excitatory-inhibitory networks A Ingrosso, LF Abbott PloS one 14 (8), e0220547, 2019	35	2019
Network reconstruction from infection cascades A Braunstein, A Ingrosso, AP Muntoni Journal of the Royal Society Interface 16 (151), 20180844, 2019	34	2019
Inference of causality in epidemics on temporal contact networks A Braunstein, A Ingrosso Scientific reports 6, 27538, 2016	31	2016
Epidemic mitigation by statistical inference from contact tracing data A Baker, I Biazzo, A Braunstein, G Catania, L Dall’Asta, A Ingrosso, ... Proceedings of the National Academy of Sciences 118 (32), e2106548118, 2021	26	2021
Data-driven emergence of convolutional structure in neural networks A Ingrosso, S Goldt Proceedings of the National Academy of Sciences 119 (40), e2201854119, 2022	19	2022
Neural networks trained with sgd learn distributions of increasing complexity M Refinetti, A Ingrosso, S Goldt International Conference on Machine Learning, 28843-28863, 2023	7	2023
Input correlations impede suppression of chaos and learning in balanced firing-rate networks R Engelken, A Ingrosso, R Khajeh, S Goedeke, LF Abbott PLOS Computational Biology 18 (12), e1010590, 2022	5	2022
From statistical inference to a differential learning rule for stochastic neural networks L Saglietti, F Gerace, A Ingrosso, C Baldassi, R Zecchina Interface Focus 8 (6), 20180033, 2018	5	2018
Optimal learning with excitatory and inhibitory synapses A Ingrosso PLoS Computational Biology 16 (12), e1008536, 2020	3	2020
Machine learning at the mesoscale: a computation-dissipation bottleneck A Ingrosso, E Panizon arXiv preprint arXiv:2307.02379, 2023	1	2023
Discovering neuronal cell types and their gene expression profiles using a spatial point process mixture model F Huang, A Anandkumar, C Borgs, J Chayes, E Fraenkel, M Hawrylycz, ... arXiv preprint arXiv:1602.01889, 2016	1	2016
Casualità, causalità e Machine Learning nel contenimento epidemico A Braunstein, L Dall'Asta, A Ingrosso Ithaca: Viaggio nella Scienza 2020 (16), 183-194, 2020		2020

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