The emergence of a concept in shallow neural networks E Agliari, F Alemanno, A Barra, G De Marzo Neural Networks 148, 232-253, 2022 | 37 | 2022 |
Neural networks with a redundant representation: Detecting the undetectable E Agliari, F Alemanno, A Barra, M Centonze, A Fachechi Physical review letters 124 (2), 028301, 2020 | 36 | 2020 |
Generalized Guerra’s interpolation schemes for dense associative neural networks E Agliari, F Alemanno, A Barra, A Fachechi Neural Networks 128, 254-267, 2020 | 34 | 2020 |
Supervised hebbian learning F Alemanno, M Aquaro, I Kanter, A Barra, E Agliari Europhysics Letters 141 (1), 11001, 2023 | 30 | 2023 |
Dreaming neural networks: rigorous results E Agliari, F Alemanno, A Barra, A Fachechi Journal of Statistical Mechanics: Theory and Experiment 2019 (8), 083503, 2019 | 24 | 2019 |
Replica symmetry breaking in dense hebbian neural networks L Albanese, F Alemanno, A Alessandrelli, A Barra Journal of Statistical Physics 189 (2), 24, 2022 | 18 | 2022 |
Outperforming RBM feature-extraction capabilities by “dreaming” mechanism A Fachechi, A Barra, E Agliari, F Alemanno IEEE transactions on neural networks and learning systems 35 (1), 1172-1181, 2022 | 16 | 2022 |
Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning E Agliari, L Albanese, F Alemanno, A Alessandrelli, A Barra, F Giannotti, ... arXiv preprint arXiv:2211.14067, 2022 | 15 | 2022 |
A transport equation approach for deep neural networks with quenched random weights E Agliari, L Albanese, F Alemanno, A Fachechi Journal of Physics A: Mathematical and Theoretical 54 (50), 505004, 2021 | 14* | 2021 |
Fully automated computational approach for precisely measuring organelle acidification with optical ph sensors A Chandra, S Prasad, F Alemanno, M De Luca, R Rizzo, R Romano, ... ACS Applied Materials & Interfaces 14 (16), 18133-18149, 2022 | 11 | 2022 |
Probing single-cell fermentation fluxes and exchange networks via pH-sensing hybrid nanofibers V Onesto, S Forciniti, F Alemanno, K Narayanankutty, A Chandra, ... ACS nano 17 (4), 3313-3323, 2022 | 6 | 2022 |
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting E Agliari, F Alemanno, M Aquaro, A Fachechi Neural Networks 177, 106389, 2024 | 5 | 2024 |
Hopfield model with planted patterns: A teacher-student self-supervised learning model F Alemanno, L Camanzi, G Manzan, D Tantari Applied Mathematics and Computation 458, 128253, 2023 | 5 | 2023 |
Interpolating between Boolean and extremely high noisy patterns through minimal dense associative memories F Alemanno, M Centonze, A Fachechi Journal of Physics A: Mathematical and Theoretical 53 (7), 074001, 2020 | 4 | 2020 |
On the Marchenko–Pastur law in analog bipartite spin-glasses E Agliari, F Alemanno, A Barra, A Fachechi Journal of Physics A: Mathematical and Theoretical 52 (25), 254002, 2019 | 4 | 2019 |
Hebbian dreaming for small datasets E Agliari, F Alemanno, M Aquaro, A Barra, F Durante, I Kanter Neural Networks 173, 106174, 2024 | 3 | 2024 |
Quantifying heterogeneity to drug response in cancer–stroma kinetics F Alemanno, M Cavo, D Delle Cave, A Fachechi, R Rizzo, E D’Amone, ... Proceedings of the National Academy of Sciences 120 (11), e2122352120, 2023 | 3 | 2023 |
Recurrent neural networks that generalize from examples and optimize by dreaming M Aquaro, F Alemanno, I Kanter, F Durante, E Agliari, A Barra arXiv preprint arXiv:2204.07954, 2022 | 3 | 2022 |
Analysis of temporal correlation in heart rate variability through maximum entropy principle in a minimal pairwise glassy model E Agliari, F Alemanno, A Barra, OA Barra, A Fachechi, LF Vento, L Moretti Scientific Reports 10 (1), 15353, 2020 | 3 | 2020 |
Ultrametric identities in glassy models of natural evolution E Agliari, F Alemanno, M Aquaro, A Barra Journal of Physics A: Mathematical and Theoretical 56 (38), 385001, 2023 | 1 | 2023 |