Keze Wang

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 551 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 122 citations into it; #14,263 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 62 citations into it; #20,989 of 35,345 by trust here, #13,544 by citations
• Model Architectures — 23 citations into it; #1,828 of 2,278 by trust here, #2,215 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Biomedical Engineering — 11 citations into it
• Graph Learning — 9 citations into it
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Keze Wang trusts overall

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

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

Who trusts Keze Wang overall

1. Earnest Paul Ijjina
2. Donggeun Yoo
3. Yonatan Geifman
4. Mélanie Ducoffe
5. Ozan Sener
6. Samarth Sinha
7. Jacob Kahn
8. William H. Beluch
9. Nick Haber
10. Jing Zhang

Papers from Keze Wang's perspective

Ordered by how strongly the researchers Keze Wang 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Cost-Effective Active Learning for Deep Image Classification (2017) — 750 citations
• Adaptively Connected Neural Networks (2019) — 55 citations
• A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition (2015) — 106 citations
• 3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks (2014) — 123 citations