| Flower: A friendly federated learning research framework DJ Beutel, T Topal, A Mathur, X Qiu, J Fernandez-Marques, Y Gao, L Sani, ... arXiv preprint arXiv:2007.14390, 2020 | 1631 | 2020 |
| SpeechBrain: A general-purpose speech toolkit M Ravanelli, T Parcollet, P Plantinga, A Rouhe, S Cornell, L Lugosch, ... arXiv preprint arXiv:2106.04624, 2021 | 1189* | 2021 |
| The pytorch-kaldi speech recognition toolkit M Ravanelli, T Parcollet, Y Bengio ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 331 | 2019 |
| A survey of quaternion neural networks T Parcollet, M Morchid, G Linares Artificial Intelligence Review 53 (4), 2957-2982, 2020 | 228 | 2020 |
| Quaternion recurrent neural networks T Parcollet, M Ravanelli, M Morchid, G Linarès, C Trabelsi, R De Mori, ... ICLR 2019, 2018 | 199 | 2018 |
| Quaternion convolutional neural networks for heterogeneous image processing T Parcollet, M Morchid, G Linarès ICASSP 2019-2019 IEEE international conference on acoustics, speech and …, 2019 | 140 | 2019 |
| Quaternion convolutional neural networks for end-to-end automatic speech recognition T Parcollet, Y Zhang, M Morchid, C Trabelsi, G Linarès, R De Mori, ... INTERSPEECH 2018, 2018 | 139 | 2018 |
| A first look into the carbon footprint of federated learning X Qiu, T Parcollet, J Fernandez-Marques, PPB Gusmao, Y Gao, DJ Beutel, ... Journal of Machine Learning Research 24 (129), 1-23, 2023 | 138 | 2023 |
| Flower: a friendly federated learning research framework (2020) DJ Beutel, T Topal, A Mathur, X Qiu, T Parcollet, ND Lane arXiv preprint arXiv:2007.14390, 2007 | 134 | 2007 |
| Lebenchmark: A reproducible framework for assessing self-supervised representation learning from speech S Evain, H Nguyen, H Le, MZ Boito, S Mdhaffar, S Alisamir, Z Tong, ... arXiv preprint arXiv:2104.11462, 2021 | 101 | 2021 |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity X Qiu, J Fernandez-Marques, PPB Gusmao, Y Gao, T Parcollet, ND Lane International Conference on Learning Representations, 2022 | 94 | 2022 |
| On-device federated learning with flower A Mathur, DJ Beutel, PPB de Gusmao, J Fernandez-Marques, T Topal, ... arXiv preprint arXiv:2104.03042, 2021 | 66 | 2021 |
| Open-source conversational ai with speechbrain 1.0 M Ravanelli, T Parcollet, A Moumen, S De Langen, C Subakan, ... Journal of Machine Learning Research 25 (333), 1-11, 2024 | 62 | 2024 |
| End-to-end speech recognition from federated acoustic models Y Gao, T Parcollet, S Zaiem, J Fernandez-Marques, PPB De Gusmao, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 55 | 2022 |
| Quaternion neural networks for spoken language understanding T Parcollet, M Morchid, PM Bousquet, R Dufour, G Linarès, R De Mori 2016 IEEE Spoken Language Technology Workshop (SLT), 362-368, 2016 | 55 | 2016 |
| The energy and carbon footprint of training end-to-end speech recognizers T Parcollet, M Ravanelli | 50 | 2021 |
| Task agnostic and task specific self-supervised learning from speech with lebenchmark S Evain, MH Nguyen, H Le, MZ Boito, S Mdhaffar, S Alisamir, Z Tong, ... Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 | 49 | 2021 |
| Adversarial disentanglement of speaker representation for attribute-driven privacy preservation PG Noé, M Mohammadamini, D Matrouf, T Parcollet, A Nautsch, ... arXiv preprint arXiv:2012.04454, 2020 | 45 | 2020 |
| E2E-SINCNET: Toward fully end-to-end speech recognition T Parcollet, M Morchid, G Linares ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 44 | 2020 |
| Can federated learning save the planet? X Qiu, T Parcollet, DJ Beutel, T Topal, A Mathur, ND Lane arXiv preprint arXiv:2010.06537, 2020 | 42 | 2020 |
Mirco RavanelliConcordia University, Université de Montréal, Mila Quebec AI InstituteVerified email at concordia.ca
