Mu Zhou

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 194 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it
• Artificial Intelligence — 39 citations into it
• Model Architectures — 23 citations into it

Whom Mu Zhou trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Karen Simonyan
9. Diederik P. Kingma
10. X. Zhang

Who trusts Mu Zhou overall

1. Wentao Zhu
2. Shuo Wang
3. Le Lu
4. Qin Liu
5. Dinggang Shen
6. Jiacheng Ruan
7. Min Chen
8. Fabian Isensee
9. Adnan Qayyum
10. Justin Ker

Papers from Mu Zhou's perspective

Ordered by how strongly the researchers Mu Zhou 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation (2021) — 631 citations
• Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019) — 369 citations
• Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation (2017) — 470 citations
• Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification (2017) — 615 citations
• Multi-scale Convolutional Neural Networks for Lung Nodule Classification (2015) — 617 citations