Alessandro Ingrosso
Alessandro Ingrosso
Senior Postdoctoral Scientist, The Abdus Salam International Centre for Theoretical Physics
Zweryfikowany adres z ictp.it - Strona główna
Cytowane przez
Cytowane przez
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
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
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
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
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
Network reconstruction from infection cascades
A Braunstein, A Ingrosso, AP Muntoni
Journal of the Royal Society Interface 16 (151), 20180844, 2019
Inference of causality in epidemics on temporal contact networks
A Braunstein, A Ingrosso
Scientific reports 6, 27538, 2016
Training dynamically balanced excitatory-inhibitory networks
A Ingrosso, LF Abbott
PloS one 14 (8), e0220547, 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), e2106548118, 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
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
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
Optimal learning with excitatory and inhibitory synapses
A Ingrosso
PLOS Computational Biology 16 (12), e1008536, 2020
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
Neural networks trained with SGD learn distributions of increasing complexity
M Refinetti, A Ingrosso, S Goldt
arXiv preprint arXiv:2211.11567, 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
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