Dong Nie

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 150 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #18,174 of 21,575 by trust here, #7,340 by citations
• Artificial Intelligence — 20 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it; #2,930 of 3,265 by trust here, #2,073 by citations
• Media Technology — 12 citations into it
• Generative Models — 8 citations into it
• Optimization & Training — 5 citations into it

Whom Dong Nie trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Yoshua Bengio
6. Individualized Treat
7. Chao Dong
8. Zhuowen Tu
9. Ilya Sutskever
10. Xavier Glorot

Who trusts Dong Nie overall

1. Sergey I. Nikolenko
2. Jelmer M. Wolterink
3. Salman Ul Hassan Dar
4. Jamie Hayes
5. Mahmut Yurt
6. Tao Zhou
7. Joseph Paul Cohen
8. Zizhao Zhang
9. Senzhang Wang
10. Maayan Frid-Adar

Papers from Dong Nie's perspective

Ordered by how strongly the researchers Dong Nie 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Medical Image Synthesis with Context-Aware Generative Adversarial Networks (2016) — 763 citations
• Medical Image Synthesis with Deep Convolutional Adversarial Networks (2018) — 562 citations
• Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI (2017) — 289 citations
• 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients (2016) — 269 citations