Riccardo Zecchina
Affiliations: | ICTP, Trieste, Grignano, Friuli-Venezia Giulia, Italy |
Area:
Statistical Physics, message-passing algorithmsGoogle:
"Riccardo Zecchina"Mean distance: 16.27 (cluster 17)
Cross-listing: Computational Biology Tree
Children
Sign in to add trainee| Alfredo Braunstein | grad student | ICTP, Trieste | |
| Demian Battaglia | grad student | 2002-2005 | ICTP, Trieste |
| Alireza Alemi | post-doc | 2013-2015 | Polytechnic of Turin |
| Enrico M. Malatesta | post-doc | 2018-2021 | (Physics Tree) |
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Publications
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| Baldassi C, Malatesta EM, Perugini G, et al. (2023) Typical and atypical solutions in nonconvex neural networks with discrete and continuous weights. Physical Review. E. 108: 024310 |
| Baldassi C, Lauditi C, Malatesta EM, et al. (2022) Learning through atypical phase transitions in overparameterized neural networks. Physical Review. E. 106: 014116 |
| Baldassi C, Lauditi C, Malatesta EM, et al. (2022) Unveiling the Structure of Wide Flat Minima in Neural Networks. Physical Review Letters. 127: 278301 |
| Baldassi C, Malatesta EM, Negri M, et al. (2020) Wide flat minima and optimal generalization in classifying high-dimensional Gaussian mixtures Journal of Statistical Mechanics: Theory and Experiment. 2020: 124012 |
| Baldassi C, Della Vecchia R, Lucibello C, et al. (2020) Clustering of solutions in the symmetric binary perceptron Journal of Statistical Mechanics: Theory and Experiment. 2020: 073303 |
| Baldassi C, Pittorino F, Zecchina R. (2019) Shaping the learning landscape in neural networks around wide flat minima. Proceedings of the National Academy of Sciences of the United States of America |
| Baldassi C, Malatesta EM, Zecchina R. (2019) Properties of the Geometry of Solutions and Capacity of Multilayer Neural Networks with Rectified Linear Unit Activations. Physical Review Letters. 123: 170602 |
| Saglietti L, Gerace F, Ingrosso A, et al. (2018) From statistical inference to a differential learning rule for stochastic neural networks. Interface Focus. 8: 20180033 |
| Baldassi C, Gerace F, Kappen HJ, et al. (2018) Role of Synaptic Stochasticity in Training Low-Precision Neural Networks. Physical Review Letters. 120: 268103 |
| Baldassi C, Zecchina R. (2018) Efficiency of quantum vs. classical annealing in nonconvex learning problems. Proceedings of the National Academy of Sciences of the United States of America |