Alessandro Ingrosso

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Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityVerified email at unibocconi.it
Carlo BaldassiBocconi University; INFN; 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 MuntoniPost-Doc, Italian Institute for Genomic MedicineVerified email at polito.it
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorVerified email at columbia.edu
Fabrizio AltarelliCapital Fund Management, 23 rue de l'Université, Paris, FranceVerified email at polito.it
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Mario DipoppaColumbia UniversityVerified email at columbia.edu
Marc MezardEcole normale supérieure ParisVerified email at ens.fr
Giovanni CataniaUniversidad Complutense MadridVerified email at ucm.es
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
Maria RefinettiEcole Normale Supérieure de ParisVerified email at ens.fr
indaco biazzoPolitecnico di TorinoVerified email at polito.it
Stefano SARAO MANNELLIGatsby Computational Neuroscience Unit & Sainsbury Wellcome Centre | University College LondonVerified email at ucl.ac.uk
Lenka ZdeborovaEPFL, SwitzerlandVerified email at epfl.ch

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	137	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	116	2015
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	49	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	37	2014
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	29	2020
Network reconstruction from infection cascades A Braunstein, A Ingrosso, AP Muntoni Journal of the Royal Society Interface 16 (151), 20180844, 2019	26	2019
Inference of causality in epidemics on temporal contact networks A Braunstein, A Ingrosso Scientific reports 6, 27538, 2016	25	2016
Training dynamically balanced excitatory-inhibitory networks A Ingrosso, LF Abbott PloS one 14 (8), e0220547, 2019	19	2019
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), 2021	7	2021
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	2	2020
Input correlations impede suppression of chaos and learning in balanced rate networks R Engelken, A Ingrosso, R Khajeh, S Goedeke, LF Abbott arXiv preprint arXiv:2201.09916, 2022	1	2022
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
Data-driven emergence of convolutional structure in neural networks A Ingrosso, S Goldt arXiv preprint arXiv:2202.00565, 2022		2022
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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