Jun Zhou

Computer Vision and Pattern Recognition · with Gao · 2020

A researcher in the Science corpus: 2 papers; trusts 436 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 123 citations into it; #20,067 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 89 citations into it; #32,724 of 35,345 by trust here, #25,809 by citations
• Model Architectures — 42 citations into it; #2,079 of 2,278 by trust here, #1,976 by citations
• Signal Processing — 32 citations into it
• Optimization & Training — 20 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Cognitive Neuroscience — 7 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Jun Zhou trusts overall

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

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

Who trusts Jun Zhou overall

1. Mohammad Mustafa Taye

Papers from Jun Zhou's perspective

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

Their papers

• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020) — 4,320 citations
• Deep Residual-Dense Lattice Network for Speech Enhancement (2020) — 42 citations