| Fitnets: Hints for thin deep nets A Romero, N Ballas, SE Kahou, A Chassang, C Gatta, Y Bengio arXiv preprint arXiv:1412.6550, 2014 | 5435 | 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 | 1991 | 2017 |
| Dinov2: Learning robust visual features without supervision M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ... arXiv preprint arXiv:2304.07193, 2023 | 1539* | 2023 |
| 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 | 1387 | 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 | 1115* | 2016 |
| Delving deeper into convolutional networks for learning video representations N Ballas, L Yao, C Pal, A Courville arXiv preprint arXiv:1511.06432, 2015 | 859 | 2015 |
| 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 | 528 | 2017 |
| Recurrent batch normalization T Cooijmans, N Ballas, C Laurent, Ç Gülçehre, A Courville arXiv preprint arXiv:1603.09025, 2016 | 507 | 2016 |
| Stochastic gradient push for distributed deep learning M Assran, N Loizou, N Ballas, M Rabbat International Conference on Machine Learning, 344-353, 2019 | 388 | 2019 |
| 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 | 384 | 2016 |
| Fitnets: Hints for thin deep nets R Adriana, B Nicolas, KS Ebrahimi, C Antoine, G Carlo, B Yoshua Proc. ICLR 2 (3), 1, 2015 | 331* | 2015 |
| Masked siamese networks for label-efficient learning M Assran, M Caron, I Misra, P Bojanowski, F Bordes, P Vincent, A Joulin, ... European Conference on Computer Vision, 456-473, 2022 | 279 | 2022 |
| A dissection of overfitting and generalization in continuous reinforcement learning A Zhang, N Ballas, J Pineau arXiv preprint arXiv:1806.07937, 2018 | 205 | 2018 |
| Improved conditional vrnns for video prediction L Castrejon, N Ballas, A Courville Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 195 | 2019 |
| Self-supervised learning from images with a joint-embedding predictive architecture M Assran, Q Duval, I Misra, P Bojanowski, P Vincent, M Rabbat, Y LeCun, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 192 | 2023 |
| Slowmo: Improving communication-efficient distributed sgd with slow momentum J Wang, V Tantia, N Ballas, M Rabbat arXiv preprint arXiv:1910.00643, 2019 | 184 | 2019 |
| 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 | 159 | 2021 |
| Residual connections encourage iterative inference S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio arXiv preprint arXiv:1710.04773, 2017 | 147 | 2017 |
| Dynamic capacity networks A Almahairi, N Ballas, T Cooijmans, Y Zheng, H Larochelle, A Courville International conference on machine learning, 2549-2558, 2016 | 143 | 2016 |
| 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 | 135 | 2018 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARE-mailová adresa ověřena na: umontreal.ca
