Neural heterogeneity promotes robust learning N Perez-Nieves, VCH Leung, PL Dragotti, DFM Goodman Nature Communications, 5791, 2021 | 160 | 2021 |
Reconstruction of FRI Signals Using Deep Neural Network Approaches VCH Leung, JJ Huang, PL Dragotti ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 13 | 2020 |
Reconstruction of FRI signals using autoencoders with fixed decoders VCH Leung, JJ Huang, YC Eldar, PL Dragotti 2021 29th European Signal Processing Conference (EUSIPCO), 1496-1500, 2021 | 5 | 2021 |
Learning-based Reconstruction of FRI Signals VCH Leung, JJ Huang, YC Eldar, PL Dragotti IEEE Transactions on Signal Processing, 2023 | 1 | 2023 |
Learning to process with spikes and to localise pulses VCH Leung Imperial College London, 2023 | | 2023 |
First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures S Liu, VCH Leung, PL Dragotti Frontiers in Neuroscience 17, 1266003, 2023 | | 2023 |
Advantages of heterogeneity of parameters in spiking neural network training N Perez-Nieves, VCH Leung, PL Dragotti, DFM Goodman 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany, 2019 | | 2019 |