Mohammad Pezeshki

According to our database, Mohammad Pezeshki authored at least 17 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Compositional Risk Minimization.

Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent

CoRR, 2024

Discovering Environments with XRM.

Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Feedback-guided Data Synthesis for Imbalanced Classification.

Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano

CoRR, 2023

Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok.

Pascal Tikeng Notsawo Jr., Hattie Zhou, Mohammad Pezeshki, Irina Rish, Guillaume Dumas

CoRR, 2023

2022

Multi-scale Feature Learning Dynamics: Insights for Double Descent.

Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie

Proceedings of the International Conference on Machine Learning, 2022

Simple data balancing achieves competitive worst-group-accuracy.

Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Gradient Starvation: A Learning Proclivity in Neural Networks.

Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2019

Negative Momentum for Improved Game Dynamics.

Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Rémi Le Priol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

On the Learning Dynamics of Deep Neural Networks.

Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron C. Courville, Yoshua Bengio

CoRR, 2018

2017

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal

CoRR, 2016

Theano: A Python framework for fast computation of mathematical expressions.

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

CoRR, 2016

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks.

Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron C. Courville

Proceedings of the 17th Annual Conference of the International Speech Communication Association, 2016

Deconstructing the Ladder Network Architecture.

Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron C. Courville, Yoshua Bengio

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Sequence Modeling using Gated Recurrent Neural Networks.

Mohammad Pezeshki

CoRR, 2015

2014

Deep Belief Networks for Image Denoising.

Mohammad Ali Keyvanrad, Mohammad Pezeshki, Mohammad Ali Homayounpour

Proceedings of the 2nd International Conference on Learning Representations, 2014

2013

Distinction between features extracted using deep belief networks.

Mohammad Pezeshki, Sajjad Gholami, Ahmad Nickabadi

CoRR, 2013