Weiwei Hu

Artificial Intelligence · with Tan · 2017

A researcher in the Science corpus: 2 papers; trusts 97 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #10,405 of 35,345 by trust here, #22,463 by citations
• Safety, Robustness & Interpretability — 23 citations into it
• Signal Processing — 17 citations into it; #1,865 of 7,572 by trust here, #4,201 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Computer Networks and Communications — 10 citations into it; #2,089 of 9,886 by trust here, #5,340 by citations
• Generative Models — 7 citations into it
• Information Systems — 6 citations into it
• Model Architectures — 6 citations into it

Whom Weiwei Hu trusts overall

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

1. Nicolas Papernot
2. Diederik P. Kingma
3. Ian J. Goodfellow
4. Ilya Sutskever
5. Yann LeCun
6. Geoffrey E. Hinton
7. Christian Szegedy
8. Yoshua Bengio
9. Jimmy Ba
10. Patrick Drew McDaniel

Who trusts Weiwei Hu overall

1. Zilong Lin
2. Nektaria Kaloudi
3. A. Serban
4. Abdul Jabbar
5. W. Zhang
6. Pin-Yu Chen
7. Liang Tong
8. Jie Gui
9. Han Xu
10. Yong-yu Shi

Papers from Weiwei Hu's perspective

Ordered by how strongly the researchers Weiwei Hu 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN (2017) — 540 citations
• Black-Box Attacks against RNN based Malware Detection Algorithms (2017) — 163 citations