| End-to-end attention-based large vocabulary speech recognition D Bahdanau, J Chorowski, D Serdyuk, P Brakel, Y Bengio 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 1172 | 2016 |
| An actor-critic algorithm for sequence prediction D Bahdanau, P Brakel, K Xu, A Goyal, R Lowe, J Pineau, A Courville, ... arXiv preprint arXiv:1607.07086, 2016 | 528 | 2016 |
| Towards end-to-end speech recognition with deep convolutional neural networks Y Zhang, M Pezeshki, P Brakel, S Zhang, CLY Bengio, A Courville arXiv preprint arXiv:1701.02720, 2017 | 375 | 2017 |
| Batch normalized recurrent neural networks C Laurent, G Pereyra, P Brakel, Y Zhang, Y Bengio 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 264 | 2016 |
| Light gated recurrent units for speech recognition M Ravanelli, P Brakel, M Omologo, Y Bengio IEEE Transactions on Emerging Topics in Computational Intelligence 2 (2), 92-102, 2018 | 234 | 2018 |
| Audio-based music classification with a pretrained convolutional network S Dieleman, P Brakel, B Schrauwen 12th International Society for Music Information Retrieval Conference (ISMIR …, 2011 | 162 | 2011 |
| Deconstructing the ladder network architecture M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio International conference on machine learning, 2368-2376, 2016 | 109 | 2016 |
| Learning independent features with adversarial nets for non-linear ica P Brakel, Y Bengio arXiv preprint arXiv:1710.05050, 2017 | 76 | 2017 |
| Invariant representations for noisy speech recognition D Serdyuk, K Audhkhasi, P Brakel, B Ramabhadran, S Thomas, Y Bengio arXiv preprint arXiv:1612.01928, 2016 | 71 | 2016 |
| Recall traces: Backtracking models for efficient reinforcement learning A Goyal, P Brakel, W Fedus, S Singhal, T Lillicrap, S Levine, H Larochelle, ... arXiv preprint arXiv:1804.00379, 2018 | 59 | 2018 |
| Improving speech recognition by revising gated recurrent units M Ravanelli, P Brakel, M Omologo, Y Bengio arXiv preprint arXiv:1710.00641, 2017 | 52 | 2017 |
| A network of deep neural networks for distant speech recognition M Ravanelli, P Brakel, M Omologo, Y Bengio 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 50 | 2017 |
| Training energy-based models for time-series imputation P Brakel, D Stroobandt, B Schrauwen The Journal of Machine Learning Research 14 (1), 2771-2797, 2013 | 44 | 2013 |
| Oger: modular learning architectures for large-scale sequential processing D Verstraeten, B Schrauwen, S Dieleman, P Brakel, P Buteneers, ... The Journal of Machine Learning Research 13 (1), 2995-2998, 2012 | 40 | 2012 |
| Batch-normalized joint training for DNN-based distant speech recognition M Ravanelli, P Brakel, M Omologo, Y Bengio 2016 IEEE Spoken Language Technology Workshop (SLT), 28-34, 2016 | 37 | 2016 |
| Task loss estimation for sequence prediction D Bahdanau, D Serdyuk, P Brakel, NR Ke, J Chorowski, A Courville, ... arXiv preprint arXiv:1511.06456, 2015 | 32 | 2015 |
| Strong systematicity in sentence processing by simple recurrent networks P Brakel, S Frank Proceedings of the Annual Meeting of the Cognitive Science Society 31 (31), 2009 | 28 | 2009 |
| Training restricted Boltzmann machines with multi-tempering: harnessing parallelization P Brakel, S Dieleman, B Schrauwen International Conference on Artificial Neural Networks, 92-99, 2012 | 25 | 2012 |
| Bidirectional truncated recurrent neural networks for efficient speech denoising P Brakel, D Stroobandt, B Schrauwen 14th Annual conference of the International Speech Communication Association, 2013 | 7 | 2013 |
| Learning coordinated terrain-adaptive locomotion by imitating a centroidal dynamics planner P Brakel, S Bohez, L Hasenclever, N Heess, K Bousmalis arXiv preprint arXiv:2111.00262, 2021 | 3 | 2021 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARZweryfikowany adres z umontreal.ca
