Zhongzheng Ren

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 840 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #8,246 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 30 citations into it; #12,957 of 35,345 by trust here, #20,836 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #1,456 of 3,145 by trust here, #2,355 by citations
• Aerospace Engineering — 8 citations into it
• Generative Models — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Model Architectures — 6 citations into it

Whom Zhongzheng Ren trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Trevor Darrell
10. Jeff Donahue

Who trusts Zhongzheng Ren overall

1. Longlong Jing
2. Yingli Tian
3. Zeyu Feng
4. Wouter Van Gansbeke
5. Jie Gui
6. Chang Xu
7. Dacheng Tao
8. Xiaodong Yang
9. Simon Vandenhende
10. Alejandro Newell

Papers from Zhongzheng Ren's perspective

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

Their papers

• Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery (2017) — 217 citations