Qinbin Li

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 231 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 256 citations into it; #13,250 of 35,345 by trust here, #11,624 by citations
• Federated & Distributed Learning — 111 citations into it
• Privacy & Security — 52 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it
• Optimization & Training — 45 citations into it
• Computer Networks and Communications — 30 citations into it
• Electrical and Electronic Engineering — 25 citations into it
• Safety, Robustness & Interpretability — 24 citations into it

Whom Qinbin Li trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. H. Brendan McMahan
9. Ross B. Girshick
10. L. Eon Bottou

Who trusts Qinbin Li overall

1. Mang Ye
2. Wenke Huang
3. Bingsheng He
4. Hangyu Zhu
5. Zeyi Wen
6. Haftay Gebreslasie Abreha
7. Alysa Ziying Tan
8. Dinh C. Nguyen
9. Bo Du
10. Romy Mi Luo

Papers from Qinbin Li's perspective

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

Their papers

• Practical Federated Gradient Boosting Decision Trees (2019) — 220 citations
• Model-Contrastive Federated Learning (2021) — 1,746 citations
• Federated Learning on Non-IID Data Silos: An Experimental Study (2021) — 1,460 citations
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019) — 1,461 citations