Mohammad Pezeshki
Mohammad Pezeshki
Verified email at umontreal.ca
TitleCited byYear
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv preprint arXiv:1605.02688, 2016
5022016
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
1942017
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
1772016
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
922016
Deconstructing the Ladder Network Architecture
M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio
arXiv preprint arXiv:1511.06430, 2015
682015
Negative momentum for improved game dynamics
G Gidel, RA Hemmat, M Pezeshki, R Lepriol, G Huang, S Lacoste-Julien, ...
arXiv preprint arXiv:1807.04740, 2018
332018
Harm de Vries, David Warde-Farley, Dustin J
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, and …, 2016
202016
Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
URL http://arxiv. org/abs/1605.02688, 2016
202016
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
10*2013
Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688, May 2016
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
URL http://arxiv. org/abs/1605.02688, 2016
82016
Deep belief networks for image denoising
MA Keyvanrad, M Pezeshki, MA Homayounpour
arXiv preprint arXiv:1312.6158, 2013
72013
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
62018
Sequence modeling using gated recurrent neural networks
M Pezeshki
arXiv preprint arXiv:1501.00299, 2015
32015
Convergence Properties of Deep Neural Networks on Separable Data
RT des Combes, M Pezeshki, S Shabanian, A Courville, Y Bengio
2018
On the Learning Dynamics of Deep Neural Networks
RT Combes, M Pezeshki, S Shabanian, A Courville, Y Bengio
arXiv preprint arXiv:1809.06848, 2018
2018
On the Learning Dynamics of Deep Neural Networks
R Tachet des Combes, M Pezeshki, S Shabanian, A Courville, Y Bengio
arXiv preprint arXiv:1809.06848, 2018
2018
Towards deep semi supervised learning
M Pezeshki
2017
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