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Mohammad Pezeshki
Mohammad Pezeshki
Adresse e-mail validée de umontreal.ca - Page d'accueil
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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
8042016
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
3612017
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
2982016
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
1892016
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
1292019
Deconstructing the Ladder Network Architecture
M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio
arXiv preprint arXiv:1511.06430, 2015
1032015
Gradient starvation: A learning proclivity in neural networks
M Pezeshki, O Kaba, Y Bengio, AC Courville, D Precup, G Lajoie
Advances in Neural Information Processing Systems 34, 2021
422021
Theano: a Python framework for fast computation of mathematical expressions. arXiv
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv preprint arXiv:1605.02688, 2016
322016
On the Learning Dynamics of Deep Neural Networks.
RT des Combes, M Pezeshki, S Shabanian, AC Courville, Y Bengio
27*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
15*2013
Deep belief networks for image denoising
MA Keyvanrad, M Pezeshki, MA Homayounpour
arXiv preprint arXiv:1312.6158, 2013
112013
Sequence modeling using gated recurrent neural networks
M Pezeshki
arXiv preprint arXiv:1501.00299, 2015
102015
Simple data balancing achieves competitive worst-group-accuracy
BY Idrissi, M Arjovsky, M Pezeshki, D Lopez-Paz
arXiv preprint arXiv:2110.14503, 2021
22021
Multi-scale Feature Learning Dynamics: Insights for Double Descent
M Pezeshki, A Mitra, Y Bengio, G Lajoie
https://arxiv.org/pdf/2112.03215.pdf, 2021
2021
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