| Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks EO Neftci, H Mostafa, F Zenke IEEE Signal Processing Magazine 36 (6), 51-63, 2019 | 1503 | 2019 |
| A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses N Qiao, H Mostafa, F Corradi, M Osswald, F Stefanini, D Sumislawska, ... Frontiers in neuroscience 9, 141, 2015 | 793 | 2015 |
| Supervised learning based on temporal coding in spiking neural networks H Mostafa IEEE transactions on neural networks and learning systems 29 (7), 3227-3235, 2017 | 471 | 2017 |
| Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization H Mostafa, X Wang International Conference on Machine Learning, 4646--4655, 2019 | 400 | 2019 |
| NullHop: A flexible convolutional neural network accelerator based on sparse representations of feature maps A Aimar, H Mostafa, E Calabrese, A Rios-Navarro, R Tapiador-Morales, ... IEEE transactions on neural networks and learning systems 30 (3), 644-656, 2018 | 339 | 2018 |
| Synaptic plasticity dynamics for deep continuous local learning (DECOLLE) J Kaiser, H Mostafa, E Neftci Frontiers in Neuroscience 14, 424, 2020 | 333 | 2020 |
| Memristor for computing: Myth or reality? S Hamdioui, S Kvatinsky, G Cauwenberghs, L Xie, N Wald, S Joshi, ... Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 …, 2017 | 155 | 2017 |
| Deep supervised learning using local errors H Mostafa, V Ramesh, G Cauwenberghs Frontiers in neuroscience 12, 608, 2018 | 150 | 2018 |
| Fast classification using sparsely active spiking networks H Mostafa, BU Pedroni, S Sheik, G Cauwenberghs 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2017 | 72 | 2017 |
| An event-based architecture for solving constraint satisfaction problems H Mostafa, LK Müller, G Indiveri Nature communications 6 (1), 8941, 2015 | 67 | 2015 |
| Towards foundation models for knowledge graph reasoning M Galkin, X Yuan, H Mostafa, J Tang, Z Zhu arXiv preprint arXiv:2310.04562, 2023 | 66 | 2023 |
| Implementation of a spike-based perceptron learning rule using TiO2−x memristors H Mostafa, A Khiat, A Serb, CG Mayr, G Indiveri, T Prodromakis Frontiers in neuroscience 9, 357, 2015 | 53 | 2015 |
| A re-configurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128 K synapses Front N Qiao, H Mostafa, F Corradi, M Osswald, F Stefanini, D Sumislawska, ... Neurosci 9 (141), 1-17, 2015 | 40 | 2015 |
| Robust federated learning through representation matching and adaptive hyper-parameters H Mostafa arXiv preprint arXiv:1912.13075, 2019 | 38 | 2019 |
| Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons E Covi, R George, J Frascaroli, S Brivio, C Mayr, H Mostafa, G Indiveri, ... Journal of Physics D: Applied Physics 51 (34), 344003, 2018 | 38 | 2018 |
| Synaptic plasticity dynamics for deep continuous local learning J Kaiser, H Mostafa, E Neftci arXiv preprint arXiv:1811.10766, 2018 | 29 | 2018 |
| Sequential aggregation and rematerialization: Distributed full-batch training of graph neural networks on large graphs H Mostafa Proceedings of Machine Learning and Systems 4, 265-275, 2022 | 26 | 2022 |
| Convolution processing apparatus and method H Mostafa, A Alessandro US Patent 10,296,829, 2019 | 21 | 2019 |
| Rhythmic inhibition allows neural networks to search for maximally consistent states H Mostafa, LK Müller, G Indiveri Neural computation 27 (12), 2510-2547, 2015 | 21 | 2015 |
| Exploiting long-term dependencies for generating dynamic scene graphs S Feng, H Mostafa, M Nassar, S Majumdar, S Tripathi Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 20 | 2023 |
