| Fitnets: Hints for thin deep nets A Romero, N Ballas, SE Kahou, A Chassang, C Gatta, Y Bengio arXiv preprint arXiv:1412.6550, 2014 | 4128 | 2014 |
| A closer look at memorization in deep networks D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... International conference on machine learning, 233-242, 2017 | 1379 | 2017 |
| Describing Videos by Exploiting Temporal Structure L Yao, A Torabi, K Cho, N Ballas, C Pal, H Larochelle, A Courville arXiv preprint arXiv:1502.08029, 2015 | 1244 | 2015 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 1067* | 2016 |
| Delving deeper into convolutional networks for learning video representations N Ballas, L Yao, C Pal, A Courville arXiv preprint arXiv:1511.06432, 2015 | 679 | 2015 |
| Recurrent batch normalization T Cooijmans, N Ballas, C Laurent, Ç Gülçehre, A Courville arXiv preprint arXiv:1603.09025, 2016 | 459 | 2016 |
| Three factors influencing minima in sgd S Jastrzębski, Z Kenton, D Arpit, N Ballas, A Fischer, Y Bengio, A Storkey arXiv preprint arXiv:1711.04623, 2017 | 408 | 2017 |
| Zoneout: Regularizing rnns by randomly preserving hidden activations D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ... arXiv preprint arXiv:1606.01305, 2016 | 346 | 2016 |
| Stochastic gradient push for distributed deep learning M Assran, N Loizou, N Ballas, M Rabbat International Conference on Machine Learning, 344-353, 2019 | 284 | 2019 |
| A dissection of overfitting and generalization in continuous reinforcement learning A Zhang, N Ballas, J Pineau arXiv preprint arXiv:1806.07937, 2018 | 163 | 2018 |
| Improved conditional vrnns for video prediction L Castrejon, N Ballas, A Courville Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 144 | 2019 |
| Slowmo: Improving communication-efficient distributed sgd with slow momentum J Wang, V Tantia, N Ballas, M Rabbat arXiv preprint arXiv:1910.00643, 2019 | 119 | 2019 |
| Dynamic capacity networks A Almahairi, N Ballas, T Cooijmans, Y Zheng, H Larochelle, A Courville International Conference on Machine Learning, 2549-2558, 2016 | 109 | 2016 |
| Residual connections encourage iterative inference S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio arXiv preprint arXiv:1710.04773, 2017 | 102 | 2017 |
| Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples M Assran, M Caron, I Misra, P Bojanowski, A Joulin, N Ballas, M Rabbat Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 97 | 2021 |
| A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering T Maharaj, N Ballas, A Rohrbach, A Courville, C Pal Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 97 | 2017 |
| Masked siamese networks for label-efficient learning M Assran, M Caron, I Misra, P Bojanowski, F Bordes, P Vincent, A Joulin, ... Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | 94 | 2022 |
| Fast approximate natural gradient descent in a kronecker factored eigenbasis T George, C Laurent, X Bouthillier, N Ballas, P Vincent Advances in Neural Information Processing Systems 31, 2018 | 91 | 2018 |
| On the relation between the sharpest directions of DNN loss and the SGD step length S Jastrzębski, Z Kenton, N Ballas, A Fischer, Y Bengio, A Storkey arXiv preprint arXiv:1807.05031, 2018 | 88* | 2018 |
| Deep nets don't learn via memorization D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... | 61 | 2017 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
