| 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 | 193 | 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 | 153 | 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 | 109 | 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 | 60 | 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 | 45 | 2014 |
| Network reconstruction from infection cascades A Braunstein, A Ingrosso, AP Muntoni Journal of the Royal Society Interface 16 (151), 20180844, 2019 | 40 | 2019 |
| Training dynamically balanced excitatory-inhibitory networks A Ingrosso, LF Abbott PloS one 14 (8), e0220547, 2019 | 38 | 2019 |
| Data-driven emergence of convolutional structure in neural networks A Ingrosso, S Goldt Proceedings of the National Academy of Sciences 119 (40), e2201854119, 2022 | 33 | 2022 |
| 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 | 30 | 2021 |
| Neural networks trained with SGD learn distributions of increasing complexity M Refinetti, A Ingrosso, S Goldt International Conference on Machine Learning, 28843-28863, 2023 | 21 | 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 | 8 | 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 | 6 | 2018 |
| Machine learning at the mesoscale: a computation-dissipation bottleneck A Ingrosso, E Panizon Physical Review E 109 (1), 014132, 2024 | 3 | 2024 |
| Optimal learning with excitatory and inhibitory synapses A Ingrosso PLOS Computational Biology 16 (12), e1008536, 2020 | 3 | 2020 |
| Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers F Bassetti, M Gherardi, A Ingrosso, M Pastore, P Rotondo arXiv preprint arXiv:2406.03260, 2024 | 1 | 2024 |
| 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 |
| Statistical mechanics of transfer learning in fully-connected networks in the proportional limit A Ingrosso, R Pacelli, P Rotondo, F Gerace arXiv preprint arXiv:2407.07168, 2024 | | 2024 |
| Sparsity Enhances Non-Gaussian Data Statistics During Local Receptive Field Formation WT Redman, Z Wang, A Ingrosso, S Goldt Conference on Parsimony and Learning (Recent Spotlight Track), 2023 | | 2023 |
| 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 |
Riccardo Zecchinaprofessor, theoretical physics, Bocconi University在 unibocconi.it 的电子邮件经过验证
