Mahmoud Assran

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 11 papers; trusts 946 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 414 citations into it; #21,427 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 401 citations into it; #11,473 of 35,345 by trust here, #5,317 by citations
• Representation & Self-Supervised Learning — 243 citations into it
• Optimization & Training — 94 citations into it; #1,391 of 2,601 by trust here, #1,448 by citations
• Evaluation & Benchmarks — 60 citations into it
• Model Architectures — 32 citations into it
• Computer Networks and Communications — 31 citations into it; #2,987 of 9,886 by trust here, #3,005 by citations
• Computational Mechanics — 28 citations into it

Whom Mahmoud Assran trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Yann LeCun
7. Diederik P. Kingma
8. Ross B. Girshick
9. Karen Simonyan
10. Ruslan Salakhutdinov

Who trusts Mahmoud Assran overall

1. Nicolas Loizou
2. Jianyu Wang
3. Anastasia Koloskova
4. Jiyan Jiang
5. Adrien Bardes
6. Navjot Singh
7. Othmane Marfoq
8. Mathilde Caron
9. Wenpeng Zhang
10. Ahmed Imteaj

Papers from Mahmoud Assran's perspective

Ordered by how strongly the researchers Mahmoud Assran trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• DINOv2: Learning Robust Visual Features without Supervision (2023) — 9,372 citations
• Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (2023) — 1,029 citations
• Masked Siamese Networks for Label-Efficient Learning (2022) — 424 citations
• Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (2021) — 175 citations
• Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations (2020) — 3 citations
• Revisiting Feature Prediction for Learning Visual Representations from Video (2024) — 396 citations
• Stochastic Gradient Push for Distributed Deep Learning (2018) — 410 citations
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025) — 527 citations
• On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings (2020) — 74 citations
• Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations. (2020) — 18 citations
• Asynchronous Gradient Push (2018) — 77 citations