Tarek M. Taha

Computer Vision and Pattern Recognition · 2011–2020

A researcher in the Science corpus: 8 papers; trusts 407 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 337 citations into it; #13,364 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 296 citations into it; #20,718 of 35,345 by trust here, #8,893 by citations
• Model Architectures — 113 citations into it
• Radiology, Nuclear Medicine and Imaging — 71 citations into it; #1,675 of 3,265 by trust here, #1,484 by citations
• Optimization & Training — 45 citations into it
• Electrical and Electronic Engineering — 37 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Biomedical Engineering — 27 citations into it

Whom Tarek M. Taha trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Kaiming He
5. Ilya Sutskever
6. Yann LeCun
7. Christian Szegedy
8. Diederik P. Kingma
9. Alex Graves
10. Sergey Ioffe

Who trusts Tarek M. Taha overall

1. Md. Zahangir Alom
2. Nahian Alam Siddique
3. Vinay Uday Prabhu

Papers from Tarek M. Taha's perspective

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

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

Their papers

• A Memristor Device Model (2011) — 297 citations
• COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches (2020) — 170 citations
• A State-of-the-Art Survey on Deep Learning Theory and Architectures (2019) — 1,450 citations
• Recurrent residual U-Net for medical image segmentation (2019) — 731 citations
• Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (2018) — 1,278 citations
• Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (2018) — 295 citations
• The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches (2018) — 1,014 citations
• Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net) (2018) — 329 citations