Chongli Qin

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 192 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #14,586 of 35,345 by trust here, #8,365 by citations
• Safety, Robustness & Interpretability — 115 citations into it
• Molecular Biology — 91 citations into it; #10,748 of 17,773 by trust here, #3,333 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Computational Biology & Bioinformatics — 53 citations into it
• Optimization & Training — 32 citations into it
• Signal Processing — 25 citations into it
• Hardware and Architecture — 21 citations into it

Whom Chongli Qin trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. Christian Szegedy
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. James Martens

Who trusts Chongli Qin overall

1. Anne-Florence Bitbol
2. Sven Gowal
3. Sylvestre-Alvise Rebuffi
4. Francesco Croce
5. Anqi Mao
6. Chen Zhu
7. Maksym Andriushchenko
8. Cihang Xie
9. Dongxian Wu
10. Matthias Hein

Papers from Chongli Qin's perspective

Ordered by how strongly the researchers Chongli 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversarial Robustness through Local Linearization (2019) — 333 citations
• On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (2018) — 640 citations
• Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations (2019) — 60 citations
• Improved protein structure prediction using potentials from deep learning (2020) — 2,936 citations
• Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) (2019) — 268 citations
• Power law tails in phylogenetic systems (2018) — 62 citations
• Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples (2020) — 378 citations