Zhan Qin

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 185 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #20,468 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 83 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it
• Signal Processing — 21 citations into it
• Privacy & Security — 16 citations into it; #446 of 709 by trust here, #404 by citations
• Model Architectures — 10 citations into it
• Optimization & Training — 5 citations into it

Whom Zhan Qin trusts overall

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

1. Ian J. Goodfellow
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Christian Szegedy
6. Radford M. Neal
7. Yoshua Bengio
8. Cynthia Dwork
9. Yann LeCun
10. Ilya Sutskever

Who trusts Zhan Qin overall

1. Tianhao Wang
2. Jinyuan Jia
3. Ninghui Li
4. Somesh Jha
5. Changchang Liu
6. Jong Wook Kim
7. Ning Wang
8. Graham Cormode
9. Neil Zhenqiang Gong
10. Amrita Roy Chowdhury

Papers from Zhan Qin's perspective

Ordered by how strongly the researchers Zhan Qin 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy (2016) — 344 citations
• Feature Importance-aware Transferable Adversarial Attacks (2021) — 329 citations
• Adversarial Attacks and Defenses in Deep Learning (2020) — 650 citations
• GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy (2019) — 209 citations
• A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing (2016) — 636 citations