Mohammad Pezeshki
Title
Cited by
Cited by
Year
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
6732016
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
2922017
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
2492016
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
1622016
Deconstructing the Ladder Network Architecture
M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio
arXiv preprint arXiv:1511.06430, 2015
932015
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
832019
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
192018
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
82013
Sequence modeling using gated recurrent neural networks
M Pezeshki
arXiv preprint arXiv:1501.00299, 2015
62015
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
52020
Towards deep semi supervised learning
M Pezeshki
2017
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Articles 1–12