Mohammad Pezeshki

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 20

Top Topics

Speech Recognizers

Evolutionary Game

Associative Search

Top Venues

Publications

Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano
Feedback-guided Data Synthesis for Imbalanced Classification. CoRR (2023)
Pascal Tikeng Notsawo Jr., Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok. CoRR (2023)
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
Discovering environments with XRM. CoRR (2023)
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
Simple data balancing achieves competitive worst-group-accuracy. CLeaR (2022)
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie
Multi-scale Feature Learning Dynamics: Insights for Double Descent. ICML (2022)
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
Gradient Starvation: A Learning Proclivity in Neural Networks. NeurIPS (2021)
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
Simple data balancing achieves competitive worst-group-accuracy. CoRR (2021)
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie
Multi-scale Feature Learning Dynamics: Insights for Double Descent. CoRR (2021)
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR (2020)
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas
Negative Momentum for Improved Game Dynamics. AISTATS (2019)
Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Rémi Le Priol, Simon Lacoste-Julien, Ioannis Mitliagkas
Negative Momentum for Improved Game Dynamics. CoRR (2018)
Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron C. Courville, Yoshua Bengio
On the Learning Dynamics of Deep Neural Networks. CoRR (2018)
Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron C. Courville
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. CoRR (2017)
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. ICLR (Poster) (2017)
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR (2016)
Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron C. Courville
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. INTERSPEECH (2016)
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio
Deconstructing the Ladder Network Architecture. ICML (2016)
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang
Theano: A Python framework for fast computation of mathematical expressions. CoRR (2016)
Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio
Deconstructing the Ladder Network Architecture. CoRR (2015)
Mohammad Pezeshki
Sequence Modeling using Gated Recurrent Neural Networks. CoRR (2015)
Mohammad Ali Keyvanrad, Mohammad Pezeshki, Mohammad Ali Homayounpour
Deep Belief Networks for Image Denoising. ICLR (Workshop Poster) (2014)
Mohammad Pezeshki, Sajjad Gholami, Ahmad Nickabadi
Distinction between features extracted using deep belief networks. CoRR (2013)