Riccardo Zecchina

Affiliations: 
ICTP, Trieste, Grignano, Friuli-Venezia Giulia, Italy 
Area:
Statistical Physics, message-passing algorithms
Google:
"Riccardo Zecchina"
Mean distance: 16.27 (cluster 17)
 
Cross-listing: Computational Biology Tree

Parents

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Tullio Regge grad student University of Turin (Physics Tree)

Children

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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, 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
Nguyen HC, Zecchina R, Berg J. (2017) Inverse statistical problems: from the inverse Ising problem to data science Advances in Physics. 66: 197-261
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