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Luca Saglietti
Tytuł
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Cytowane przez
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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
1452016
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
1202015
Gaussian process prior variational autoencoders
FP Casale, A Dalca, L Saglietti, J Listgarten, N Fusi
Advances in neural information processing systems 31, 2018
752018
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
502016
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
292016
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
222018
Generalized approximate survey propagation for high-dimensional estimation
C Lucibello, L Saglietti, Y Lu
International Conference on Machine Learning, 4173-4182, 2019
62019
Solvable model for inheriting the regularization through knowledge distillation
L Saglietti, L Zdeborová
Mathematical and Scientific Machine Learning, 809-846, 2022
52022
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
52018
Probing transfer learning with a model of synthetic correlated datasets
F Gerace, L Saglietti, SS Mannelli, A Saxe, L Zdeborová
Machine Learning: Science and Technology 3 (1), 015030, 2022
42022
Large deviations for the perceptron model and consequences for active learning
H Cui, L Saglietti, L Zdeborová
Mathematical and Scientific Machine Learning, 390-430, 2020
32020
From inverse problems to learning: a statistical mechanics approach
C Baldassi, F Gerace, L Saglietti, R Zecchina
Journal of Physics: Conference Series 955 (1), 012001, 2018
32018
Large deviations in the perceptron model and consequences for active learning
H Cui, L Saglietti, L Zdeborovà
Machine Learning: Science and Technology 2 (4), 045001, 2021
12021
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
L Saglietti, SS Mannelli, A Saxe
arXiv preprint arXiv:2106.08068, 2021
12021
Generalized Approximate Survey Propagation for High-Dimensional Estimation: Supplementary Material
L Saglietti, Y Lu, C Lucibello
arXiv preprint arXiv:1905.05313, 0
1
Inducing bias is simpler than you think
SS Mannelli, F Gerace, N Rostamzadeh, L Saglietti
arXiv preprint arXiv:2205.15935, 2022
2022
Inducing bias is simpler than you think
S Sarao Mannelli, F Gerace, N Rostamzadeh, L Saglietti
arXiv e-prints, arXiv: 2205.15935, 2022
2022
Large deviations of semisupervised learning in the stochastic block model
H Cui, L Saglietti, L Zdeborová
Physical Review E 105 (3), 034108, 2022
2022
Out of Equilibrium Statistical Physics of Learning
L Saglietti
Politecnico di Torino, 2018
2018
Group ID U13825
F Behrens, LA Clarte, I Coke, HC Cui, F Gerace, G Piccioli, L Saglietti, ...
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