Vijayan K. Asari

Computer Vision and Pattern Recognition · 2004–2020

A researcher in the Science corpus: 8 papers; trusts 409 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 341 citations into it; #10,588 of 21,575 by trust here, #6,281 by citations
• Artificial Intelligence — 297 citations into it; #20,720 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; #1,677 of 3,265 by trust here, #1,484 by citations
• Optimization & Training — 45 citations into it
• Representation & Self-Supervised Learning — 32 citations into it
• Electrical and Electronic Engineering — 31 citations into it
• Biomedical Engineering — 27 citations into it

Whom Vijayan K. Asari trusts overall

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

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

Who trusts Vijayan K. Asari overall

1. Xiaolin Wu
2. Nahian Alam Siddique
3. Xi Zhang

Papers from Vijayan K. Asari's perspective

Ordered by how strongly the researchers Vijayan K. Asari 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Going deeper with convolutions
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• 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
• An improved face recognition technique based on modular PCA approach (2004) — 566 citations