Min Wu

Artificial Intelligence · with Huang · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #9,992 of 35,345 by trust here, #7,310 by citations
• Safety, Robustness & Interpretability — 133 citations into it; #790 of 2,251 by trust here, #782 by citations
• Computer Vision and Pattern Recognition — 76 citations into it
• Signal Processing — 23 citations into it
• Software — 20 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Program Analysis & Verification — 17 citations into it; #70 of 301 by trust here, #17 by citations
• Hardware and Architecture — 9 citations into it; #310 of 1,551 by trust here, #182 by citations

Whom Min Wu trusts overall

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

1. Ian J. Goodfellow
2. Xiaowei Huang
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Guy Katz
6. Nicholas Carlini
7. Alex Krizhevsky
8. Yann LeCun
9. Yoshua Bengio
10. Nicolas Papernot

Who trusts Min Wu overall

1. Wenjie Ruan
2. Xiaowei Huang
3. Eric Wong
4. Youcheng Sun
5. Jie M. Zhang
6. J. Kolter
7. Guy Katz

Papers from Min Wu's perspective

Ordered by how strongly the researchers Min Wu 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Safety Verification of Deep Neural Networks (2016) — 1,011 citations
• A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability (2018) — 607 citations
• Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm (2018) — 38 citations
• Concolic Testing for Deep Neural Networks (2018) — 351 citations