| Hebbian learning meets deep convolutional neural networks G Amato, F Carrara, F Falchi, C Gennaro, G Lagani Image Analysis and Processing–ICIAP 2019: 20th International Conference …, 2019 | 66 | 2019 |
| Hebbian Semi-Supervised Learning in a Sample Efficiency Setting G Lagani, F Fabrizio, C Gennaro, G Amato Neural Networks 143, 719-731, 2021 | 33 | 2021 |
| Comparing the performance of Hebbian against backpropagation learning using convolutional neural networks G Lagani, F Falchi, C Gennaro, G Amato Neural Computing and Applications 34 (8), 6503-6519, 2022 | 28 | 2022 |
| Hebbian learning algorithms for training convolutional neural networks G Lagani | 14 | 2019 |
| Evaluating hebbian learning in a semi-supervised setting G Lagani, F Falchi, C Gennaro, G Amato International Conference on Machine Learning, Optimization, and Data Science …, 2021 | 13 | 2021 |
| Training convolutional neural networks with competitive hebbian learning approaches G Lagani, F Falchi, C Gennaro, G Amato International Conference on Machine Learning, Optimization, and Data Science …, 2021 | 11 | 2021 |
| Fasthebb: Scaling hebbian training of deep neural networks to imagenet level G Lagani, C Gennaro, H Fassold, G Amato International Conference on Similarity Search and Applications, 251-264, 2022 | 8 | 2022 |
| Deep features for cbir with scarce data using hebbian learning G Lagani, D Bacciu, C Gallicchio, F Falchi, C Gennaro, G Amato Proceedings of the 19th International Conference on Content-based Multimedia …, 2022 | 8 | 2022 |
| Assessing pattern recognition performance of neuronal cultures through accurate simulation G Lagani, R Mazziotti, F Falchi, C Gennaro, GM Cicchini, T Pizzorusso, ... 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER …, 2021 | 8 | 2021 |
| Spiking neural networks and bio-inspired supervised deep learning: a survey G Lagani, F Falchi, C Gennaro, G Amato arXiv preprint arXiv:2307.16235, 2023 | 7 | 2023 |
| Bio-Inspired Approaches for Deep Learning: From Spiking Neural Networks to Hebbian Plasticity G Lagani | 3 | 2023 |
| Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey G Lagani, F Falchi, C Gennaro, G Amato arXiv preprint arXiv:2307.16236, 2023 | 2 | 2023 |
| AIMH Lab for a Susteinable Bio-Inspired AI. G Lagani, F Falchi, C Gennaro, G Amato Ital-IA, 575-584, 2023 | 1 | 2023 |
| Scaling Bio-Inspired Neural Features to Real-World Image Retrieval Problems. G Lagani SEBD, 711-717, 2023 | 1 | 2023 |
| AIMH Research Activities 2022 N Aloia, G Amato, V Bartalesi, F Benedetti, P Bolettieri, D Cafarelli, ... CNR, 2022 | 1 | 2022 |
| Recent Advancements on Bio-Inspired Hebbian Learning for Deep Neural Networks. G Lagani SEBD, 610-615, 2022 | 1 | 2022 |
| Scalable bio-inspired training of deep neural networks with FastHebb G Lagani, F Falchi, C Gennaro, H Fassold, G Amato Neurocomputing, 127867, 2024 | | 2024 |
| AIMIR Research Activities 2019 G Amato, P Bolettieri, F Carrara, L Ciampi, M Di Benedetto, F Debole, ... | | |
