| 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 | 200 | 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 | 161 | 2015 |
| A Scaling Hypothesis for the Euclidean Bipartite Matching Problem S Caracciolo, C Lucibello, G Parisi, G Sicuro Phys. Rev. E 90, 012118, 2014 | 92 | 2014 |
| 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 | 64 | 2016 |
| Entropic gradient descent algorithms and wide flat minima F Pittorino, C Lucibello, C Feinauer, G Perugini, C Baldassi, ... Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124015, 2021 | 36 | 2021 |
| Learning may need only a few bits of synaptic precision C Baldassi, F Gerace, C Lucibello, L Saglietti, R Zecchina Physical Review E 93 (5), 052313, 2016 | 35 | 2016 |
| Finite size corrections to disordered Ising models on Random Regular Graphs C Lucibello, F Morone, G Parisi, F Ricci-Tersenghi, T Rizzo Phys. Rev. E 90, 012146, 2014 | 31 | 2014 |
| Finite-size corrections to disordered systems on Erdös-Rényi random graphs U Ferrari, C Lucibello, F Morone, G Parisi, F Ricci-Tersenghi, T Rizzo Physical Review B—Condensed Matter and Materials Physics 88 (18), 184201, 2013 | 30 | 2013 |
| Loop expansion around the Bethe approximation through the M-layer construction A Altieri, MC Angelini, C Lucibello, G Parisi, F Ricci-Tersenghi, T Rizzo Journal of Statistical Mechanics: Theory and Experiment 2017 (11), 113303, 2017 | 27 | 2017 |
| Role of synaptic stochasticity in training low-precision neural networks C Baldassi, F Gerace, HJ Kappen, C Lucibello, L Saglietti, E Tartaglione, ... Physical review letters 120 (26), 268103, 2018 | 26 | 2018 |
| Exponential capacity of dense associative memories C Lucibello, M Mézard Physical Review Letters 132 (7), 077301, 2024 | 24 | 2024 |
| Unexpected upper critical dimension for spin glass models in a field predicted by the loop expansion around the bethe solution at zero temperature MC Angelini, C Lucibello, G Parisi, G Perrupato, F Ricci-Tersenghi, ... Physical review letters 128 (7), 075702, 2022 | 22 | 2022 |
| Clustering of solutions in the symmetric binary perceptron C Baldassi, R Della Vecchia, C Lucibello, R Zecchina Journal of Statistical Mechanics: Theory and Experiment 2020 (7), 073303, 2020 | 18 | 2020 |
| Deep learning via message passing algorithms based on belief propagation C Lucibello, F Pittorino, G Perugini, R Zecchina Machine Learning: Science and Technology 3 (3), 035005, 2022 | 16 | 2022 |
| One-loop diagrams in the random Euclidean matching problem C Lucibello, G Parisi, G Sicuro Physical Review E 95 (1), 012302, 2017 | 16 | 2017 |
| Star-shaped space of solutions of the spherical negative perceptron BL Annesi, C Lauditi, C Lucibello, EM Malatesta, G Perugini, F Pittorino, ... Physical Review Letters 131 (22), 227301, 2023 | 13 | 2023 |
| The statistical mechanics of random set packing and a generalization of the Karp-Sipser algorithm C Lucibello, F Ricci-Tersenghi International Journal of Statistical Mechanics 2014, 1-13, 2014 | 13 | 2014 |
| Loop expansion around the Bethe solution for the random magnetic field Ising ferromagnets at zero temperature MC Angelini, C Lucibello, G Parisi, F Ricci-Tersenghi, T Rizzo Proceedings of the National Academy of Sciences 117 (5), 2268-2274, 2020 | 12 | 2020 |
| Storage and learning phase transitions in the random-features hopfield model M Negri, C Lauditi, G Perugini, C Lucibello, E Malatesta Physical Review Letters 131 (25), 257301, 2023 | 10* | 2023 |
| Generalized approximate survey propagation for high-dimensional estimation C Lucibello, L Saglietti, Y Lu International Conference on Machine Learning, 4173-4182, 2019 | 10 | 2019 |
Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityEmail verificata su unibocconi.it
