Kaiqin Hu

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 41 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 9 citations into it
• Natural Language Processing — 6 citations into it

Whom Kaiqin Hu trusts overall

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

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

Papers from Kaiqin Hu's perspective

Ordered by how strongly the researchers Kaiqin Hu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Is normalization indispensable for training deep neural network? (2020) — 68 citations