Distinction between features extracted using deep belief networks. - Mohammad Pezeshki; Sajjad Gholami; Ahmad Nickabadi (CoRR, 2013)

Deep Belief Networks for Image Denoising. - Mohammad Ali Keyvanrad; Mohammad Pezeshki; Mohammad Ali Homayounpour (ICLR (Workshop Poster), 2014)

Sequence Modeling using Gated Recurrent Neural Networks. - Mohammad Pezeshki (CoRR, 2015)

Deconstructing the Ladder Network Architecture. - Mohammad Pezeshki; Linxi Fan; Philemon Brakel; Aaron C. Courville; Yoshua Bengio (ICML, 2016)

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. - Ying Zhang 033; Mohammad Pezeshki; Philémon Brakel; Saizheng Zhang; César Laurent; Yoshua Bengio; Aaron C. Courville (INTERSPEECH, 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; et al. (CoRR, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Aaron C. Courville; Chris Pal (CoRR, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Aaron C. Courville; Christopher J. Pal (ICLR (Poster), 2017)

On the Learning Dynamics of Deep Neural Networks. - Remi Tachet des Combes; Mohammad Pezeshki; Samira Shabanian; Aaron C. Courville; Yoshua Bengio (CoRR, 2018)

Negative Momentum for Improved Game Dynamics. - Gauthier Gidel; Reyhane Askari Hemmat; Mohammad Pezeshki; Rémi Le Priol; Gabriel Huang; Simon Lacoste-Julien; Ioannis Mitliagkas (AISTATS, 2019)

Gradient Starvation: A Learning Proclivity in Neural Networks. - Mohammad Pezeshki; Sékou-Oumar Kaba; Yoshua Bengio; Aaron C. Courville; Doina Precup; Guillaume Lajoie (NeurIPS, 2021)

Simple data balancing achieves competitive worst-group-accuracy. - Badr Youbi Idrissi; Martín Arjovsky; Mohammad Pezeshki; David Lopez-Paz (CLeaR, 2022)

Multi-scale Feature Learning Dynamics: Insights for Double Descent. - Mohammad Pezeshki; Amartya Mitra; Yoshua Bengio; Guillaume Lajoie (ICML, 2022)

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)

Discovering Environments with XRM. - Mohammad Pezeshki; Diane Bouchacourt; Mark Ibrahim; Nicolas Ballas; Pascal Vincent; David Lopez-Paz (ICML, 2024)

Compositional Risk Minimization. - Divyat Mahajan; Mohammad Pezeshki; Ioannis Mitliagkas; Kartik Ahuja; Pascal Vincent (CoRR, 2024)

Feedback-guided Data Synthesis for Imbalanced Classification. - Reyhane Askari Hemmat; Mohammad Pezeshki; Florian Bordes; Michal Drozdzal; Adriana Romero-Soriano (Trans. Mach. Learn. Res., 2024)

The Pitfalls of Memorization: When Memorization Hurts Generalization. - Reza Bayat; Mohammad Pezeshki; Elvis Dohmatob; David Lopez-Paz; Pascal Vincent (ICLR, 2025)

Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification. - Konstantin Donhauser; Charles Arnal; Mohammad Pezeshki; Vivien Cabannes; David Lopez-Paz; Kartik Ahuja (CoRR, 2025)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice. - Reyhane Askari Hemmat; Mohammad Pezeshki; Elvis Dohmatob; Florian Bordes; Pietro Astolfi; Melissa Hall; Jakob Verbeek; Michal Drozdzal; Adriana Romero-Soriano (CoRR, 2025)

Steering Large Language Model Activations in Sparse Spaces. - Reza Bayat; Ali Rahimi-Kalahroudi; Mohammad Pezeshki; Sarath Chandar; Pascal Vincent (CoRR, 2025)