Fangzhou Liao

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 213 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #7,387 of 35,345 by trust here, #4,353 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #8,908 of 21,575 by trust here, #5,754 by citations
• Safety, Robustness & Interpretability — 49 citations into it; #542 of 2,251 by trust here, #439 by citations
• Model Architectures — 24 citations into it
• Optimization & Training — 15 citations into it
• Signal Processing — 15 citations into it
• Media Technology — 8 citations into it
• Biophysics — 5 citations into it

Whom Fangzhou Liao trusts overall

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

1. Kaiming He
2. Christian Szegedy
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Yann LeCun
8. Sergey Ioffe
9. Shaoqing Ren
10. Yoshua Bengio

Who trusts Fangzhou Liao overall

1. Yinpeng Dong
2. David Stutz
3. Tianyu Pang
4. Cihang Xie
5. Lukas Schott
6. Yuhang Zhou
7. Anirban Chakraborty
8. Matthias Hein
9. Micah Goldblum
10. Eric Wong

Papers from Fangzhou Liao's perspective

Ordered by how strongly the researchers Fangzhou Liao 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Boosting Adversarial Attacks with Momentum (2017) — 3,379 citations
• Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser (2017) — 1,036 citations
• Adversarial Attacks and Defences Competition (2018) — 353 citations
• Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017) — 468 citations