Qi Qin

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 376 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 172 citations into it; #14,668 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 106 citations into it; #16,766 of 21,575 by trust here, #16,787 by citations
• Meta & Continual Learning — 71 citations into it; #712 of 1,041 by trust here, #782 by citations
• Representation & Self-Supervised Learning — 52 citations into it
• Model Architectures — 17 citations into it; #1,968 of 2,278 by trust here, #2,128 by citations
• Reinforcement Learning — 16 citations into it
• Natural Language Processing — 14 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Qi Qin trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Kaiming He
6. Diederik P. Kingma
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Sepp Hochreiter
10. Ross B. Girshick

Who trusts Qi Qin overall

1. Zixuan Ke
2. Bing Liu
3. Hu Xu
4. Jorge Armando Mendez Mendez
5. Yiduo Guo
6. Grzegorz Rype's'c
7. Wenpeng Hu
8. Liyuan Wang
9. Gyuhak Kim
10. Eric Eaton

Papers from Qi Qin's perspective

Ordered by how strongly the researchers Qi Qin 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• BNS: Building Network Structures Dynamically for Continual Learning (2021) — 48 citations
• Continual Learning by Using Information of Each Class Holistically (2021) — 66 citations
• HRN: A Holistic Approach to One Class Learning (2020) — 71 citations
• Using the Past Knowledge to Improve Sentiment Classification (2020) — 11 citations