Luca Saglietti
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Citata da
Citata da
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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
1232016
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
1002015
Gaussian process prior variational autoencoders
FP Casale, AV Dalca, L Saglietti, J Listgarten, N Fusi
arXiv preprint arXiv:1810.11738, 2018
452018
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
432016
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
252016
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
232018
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
Generalized approximate survey propagation for high-dimensional estimation
C Lucibello, L Saglietti, Y Lu
International Conference on Machine Learning, 4173-4182, 2019
42019
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
Solvable Model for Inheriting the Regularization through Knowledge Distillation
L Saglietti, L Zdeborová
arXiv preprint arXiv:2012.00194, 2020
22020
Probing transfer learning with a model of synthetic correlated datasets
F Gerace, L Saglietti, SS Mannelli, A Saxe, L Zdeborová
arXiv preprint arXiv:2106.05418, 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
Large Deviations of Semi-supervised Learning in the Stochastic Block Model
H Cui, L Saglietti, L Zdeborová
arXiv preprint arXiv:2108.00847, 2021
2021
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
L Saglietti, SS Mannelli, A Saxe
arXiv preprint arXiv:2106.08068, 2021
2021
Out of Equilibrium Statistical Physics of Learning
L Saglietti
Politecnico di Torino, 2018
2018
Role of synaptic stochasticity in training low-precision neural networks Download PDF
C Baldassi, F Gerace, HJ Kappen, C Lucibello, L Saglietti, E Tartaglione, ...
Il sistema al momento non puň eseguire l'operazione. Riprova piů tardi.
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