Mahmoud Assran

Orcid: 0000-0001-9159-8447

According to our database1, Mahmoud Assran authored at least 17 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Learning and Leveraging World Models in Visual Representation Learning.
CoRR, 2024

2023
Predicting masked tokens in stochastic locations improves masked image modeling.
CoRR, 2023

DINOv2: Learning Robust Visual Features without Supervision.
CoRR, 2023

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
The Hidden Uniform Cluster Prior in Self-Supervised Learning.
CoRR, 2022

Masked Siamese Networks for Label-Efficient Learning.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Asynchronous Gradient Push.
IEEE Trans. Autom. Control., 2021

Memory Augmented Optimizers for Deep Learning.
CoRR, 2021

Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Advances in Asynchronous Parallel and Distributed Optimization.
Proc. IEEE, 2020

A Closer Look at Codistillation for Distributed Training.
CoRR, 2020

Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations.
CoRR, 2020

On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning.
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.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Asynchronous Subgradient-Push.
CoRR, 2018

2017
An empirical comparison of multi-agent optimization algorithms.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017


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