Xuanqing Liu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 123 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #13,430 of 35,345 by trust here, #7,606 by citations
• Safety, Robustness & Interpretability — 59 citations into it; #1,408 of 2,251 by trust here, #1,512 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #19,280 of 21,575 by trust here, #18,266 by citations
• Model Architectures — 15 citations into it; #1,479 of 2,278 by trust here, #1,378 by citations
• Signal Processing — 15 citations into it; #4,422 of 7,572 by trust here, #2,990 by citations
• Optimization & Training — 13 citations into it
• Hardware and Architecture — 10 citations into it
• Statistical and Nonlinear Physics — 9 citations into it

Whom Xuanqing Liu trusts overall

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

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

Who trusts Xuanqing Liu overall

1. David Stutz
2. Adnan Siraj Rakin
3. Huan Zhang
4. Matthias Hein
5. Ulme Wennberg
6. Yandong Li
7. Kui Ren
8. Zhezhi He
9. Yinpeng Dong
10. Cihang Xie

Papers from Xuanqing Liu's perspective

Ordered by how strongly the researchers Xuanqing Liu 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. Going deeper with convolutions

Their papers

• Towards Robust Neural Networks via Random Self-ensemble (2017) — 462 citations
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (2019) — 1,573 citations
• Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network (2018) — 187 citations
• Learning to Encode Position for Transformer with Continuous Dynamical Model (2020) — 141 citations
• Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise (2019) — 171 citations