C. Yakopcic

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 7 papers; trusts 402 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 335 citations into it; #12,608 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 294 citations into it; #20,074 of 35,345 by trust here, #9,092 by citations
• Model Architectures — 113 citations into it
• Radiology, Nuclear Medicine and Imaging — 58 citations into it; #1,900 of 3,265 by trust here, #1,550 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 — 25 citations into it

Whom C. Yakopcic trusts overall

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

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

Who trusts C. Yakopcic overall

1. Md. Zahangir Alom
2. Nahian Alam Siddique
3. Reza Azad
4. Bingzhi Chen
5. Shahar Kvatinsky
6. Qiangguo Jin
7. Nishil Talati

Papers from C. Yakopcic's perspective

Ordered by how strongly the researchers C. Yakopcic 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. 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
• 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