Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv, arXiv: 1605.02688, 2016 | 666 | 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 | 284 | 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 | 239 | 2016 |
Theano: A Python framework for fast computation of mathematical expressions TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ... arXiv preprint arXiv:1605.02688, 2016 | 161 | 2016 |
Deconstructing the Ladder Network Architecture M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio arXiv preprint arXiv:1511.06430, 2015 | 90 | 2015 |
Negative momentum for improved game dynamics G Gidel, RA Hemmat, M Pezeshki, R Le Priol, G Huang, S Lacoste-Julien, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 81 | 2019 |
On the learning dynamics of deep neural networks RT des Combes, M Pezeshki, S Shabanian, A Courville, Y Bengio arXiv preprint arXiv:1809.06848, 2018 | 16 | 2018 |
Comparison three methods of clustering: K-means, spectral clustering and hierarchical clustering K Kowsari, T Borsche, A Ulbig, G Andersson, AM Saxe, JL McClelland, ... arXiv Preprint, 2013 | 13* | 2013 |
Deep belief networks for image denoising MA Keyvanrad, M Pezeshki, MA Homayounpour arXiv preprint arXiv:1312.6158, 2013 | 8 | 2013 |
Sequence modeling using gated recurrent neural networks M Pezeshki arXiv preprint arXiv:1501.00299, 2015 | 6 | 2015 |
Gradient Starvation: A Learning Proclivity in Neural Networks M Pezeshki, SO Kaba, Y Bengio, A Courville, D Precup, G Lajoie arXiv preprint arXiv:2011.09468, 2020 | 3 | 2020 |
Towards deep semi supervised learning M Pezeshki | | 2017 |