Qiang Liu

Artificial Intelligence · with Cai · 2018

A researcher in the Science corpus: 2 papers; trusts 39 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it
• Safety, Robustness & Interpretability — 67 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Signal Processing — 35 citations into it
• Computer Networks and Communications — 28 citations into it
• Privacy & Security — 22 citations into it
• Information Systems — 14 citations into it
• Model Architectures — 7 citations into it

Whom Qiang Liu trusts overall

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

1. Battista Biggio
2. Geoffrey E. Hinton
3. Ian J. Goodfellow
4. Blaine Nelson
5. Alex Krizhevsky
6. Christian Szegedy
7. Nicolas Papernot
8. Michael Brückner
9. Yoshua Bengio
10. Pavel Laskov

Papers from Qiang Liu's perspective

Ordered by how strongly the researchers Qiang Liu 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View (2018) — 390 citations
• A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture (2018) — 507 citations