Mohamed Sakr

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 10 citations into it

Whom Mohamed Sakr trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Andrew Zisserman
4. Mehdi Gheisari
5. Fung Fung Ting
6. Christian Szegedy

Papers from Mohamed Sakr's perspective

Ordered by how strongly the researchers Mohamed Sakr 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Rethinking the Inception Architecture for Computer Vision
6. Long Short-Term Memory
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021) — 326 citations