Mahmudul Hasan

Computer Vision and Pattern Recognition · Electronics · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 671 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 262 citations into it; #12,520 of 21,575 by trust here, #5,987 by citations
• Artificial Intelligence — 201 citations into it; #20,087 of 35,345 by trust here, #8,007 by citations
• Model Architectures — 68 citations into it
• Radiology, Nuclear Medicine and Imaging — 44 citations into it
• Optimization & Training — 32 citations into it
• Natural Language Processing — 26 citations into it
• Biomedical Engineering — 24 citations into it
• Representation & Self-Supervised Learning — 20 citations into it

Whom Mahmudul Hasan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ilya Sutskever
4. Kaiming He
5. Karen Simonyan
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. Navneet Dalal
10. Alex Graves

Who trusts Mahmudul Hasan overall

1. Antonino Furnari
2. Trong-Nguyen Nguyen
3. Waqas Sultani
4. Weixin Luo
5. Jianfeng Feng
6. Dilip Ravi Kiran
7. Hyunjong Park
8. Samet Akcay
9. Radu Tudor Ionescu
10. Yu-xin Zhang

Papers from Mahmudul Hasan's perspective

Ordered by how strongly the researchers Mahmudul Hasan 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
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

• Learning Temporal Regularity in Video Sequences (2016) — 1,362 citations
• Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos (2017) — 90 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