Mido Assran

According to our database, Mido Assran authored at least 31 papers between 2017 and 2026.

Collaborative distances:

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

2026

Hierarchical Planning with Latent World Models.

Wancong Zhang, Basile Terver, Artem Zholus, Soham Chitnis, Harsh Sutaria, Mido Assran, Randall Balestriero, Amir Bar, Adrien Bardes, Yann LeCun, Nicolas Ballas

CoRR, April, 2026

V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning.

Lorenzo Mur-Labadia, Matthew J. Muckley, Amir Bar, Mido Assran, Koustuv Sinha, Mike Rabbat, Yann LeCun, Nicolas Ballas, Adrien Bardes

CoRR, March, 2026

2025

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew J. Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas

CoRR, June, 2025

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D.

Sergio Arnaud, Paul McVay, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas, Ruslan Partsey, Daniel Dugas, Abha Gejji, Alexander Sax, Vincent-Pierre Berges, Mikael Henaff, Ayush Jain, Ang Cao, Ishita Prasad, Mrinal Kalakrishnan, Michael Rabbat, Nicolas Ballas, Mido Assran, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier

CoRR, April, 2025

Intuitive physics understanding emerges from self-supervised pretraining on natural videos.

Quentin Garrido, Nicolas Ballas, Mahmoud Assran, Adrien Bardes, Laurent Najman, Michael Rabbat, Emmanuel Dupoux, Yann LeCun

CoRR, February, 2025

SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision.

Maxime Poli, Mahi Luthra, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Jiayi Shen, Robin Algayres, Yu-An Chung, Mido Assran, Juan Pino, Emmanuel Dupoux

Trans. Mach. Learn. Res., 2025

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs.

Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran

Trans. Mach. Learn. Res., 2025

LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D.

Paul McVay, Sergio Arnaud, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas, Ruslan Partsey, Daniel Dugas, Abha Gejji, Alexander Sax, Vincent-Pierre Berges, Mikael Henaff, Ayush Jain, Ang Cao, Ishita Prasad, Mrinal Kalakrishnan, Michael Rabbat, Nicolas Ballas, Mido Assran, Oleksandr Maksymets, Aravind Rajeswaran

Proceedings of the Forty-second International Conference on Machine Learning, 2025

An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels.

Duy-Kien Nguyen, Mido Assran, Unnat Jain, Martin R. Oswald, Cees G. M. Snoek, Xinlei Chen

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning.

Han Lin, Tushar Nagarajan, Nicolas Ballas, Mido Assran, Mojtaba Komeili, Mohit Bansal, Koustuv Sinha

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

DINOv2: Learning Robust Visual Features without Supervision.

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski

Trans. Mach. Learn. Res., 2024

Revisiting Feature Prediction for Learning Visual Representations from Video.

Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, Nicolas Ballas

Trans. Mach. Learn. Res., 2024

Learning and Leveraging World Models in Visual Representation Learning.

Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, Yann LeCun

CoRR, 2024

Modeling Caption Diversity in Contrastive Vision-Language Pretraining.

Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas

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

Stochastic positional embeddings improve masked image modeling.

Amir Bar, Florian Bordes, Assaf Shocher, Mido Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun

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

2023

Predicting masked tokens in stochastic locations improves masked image modeling.

Amir Bar, Florian Bordes, Assaf Shocher, Mahmoud Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun

CoRR, 2023

RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data.

Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma, Mido Assran, Ishan Misra, Licheng Yu, Sean Bell

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The hidden uniform cluster prior in self-supervised learning.

Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Nicolas Ballas

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, Nicolas Ballas

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Memory Augmented Optimizers for Deep Learning.

Paul-Aymeric Martin McRae, Prasanna Parthasarathi, Mido Assran, Sarath Chandar

Proceedings of the Tenth International Conference on Learning Representations, 2022

Masked Siamese Networks for Label-Efficient Learning.

Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, Nicolas Ballas

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Asynchronous Gradient Push.

Mahmoud Assran, Michael G. Rabbat

IEEE Trans. Autom. Control., 2021

Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples.

Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, Michael G. Rabbat

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Advances in Asynchronous Parallel and Distributed Optimization.

Mahmoud Assran, Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson, Michael G. Rabbat

Proc. IEEE, 2020

A Closer Look at Codistillation for Distributed Training.

Shagun Sodhani, Olivier Delalleau, Mahmoud Assran, Koustuv Sinha, Nicolas Ballas, Michael G. Rabbat

CoRR, 2020

Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations.

Mahmoud Assran, Nicolas Ballas, Lluís Castrejón, Michael G. Rabbat

CoRR, 2020

On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings.

Mahmoud Assran, Mike Rabbat

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning.

Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Gradient Push for Distributed Deep Learning.

Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Asynchronous Subgradient-Push.

Mahmoud Assran, Michael G. Rabbat

CoRR, 2018

2017

An empirical comparison of multi-agent optimization algorithms.

Mahmoud Assran, Michael G. Rabbat

Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017