Wenjie Ruan

Artificial Intelligence · 2018

A researcher in the Science corpus: 4 papers; trusts 59 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 150 citations into it; #13,155 of 35,345 by trust here, #14,718 by citations
• Safety, Robustness & Interpretability — 127 citations into it; #1,076 of 2,251 by trust here, #1,945 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #9,045 of 21,575 by trust here, #12,172 by citations
• Program Analysis & Verification — 22 citations into it
• Software — 20 citations into it
• Electrical and Electronic Engineering — 16 citations into it; #4,820 of 16,539 by trust here, #11,641 by citations
• Computational Theory and Mathematics — 15 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Wenjie Ruan trusts overall

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

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

Who trusts Wenjie Ruan overall

1. Alexey Ignatiev
2. Youcheng Sun
3. Xiaowei Huang
4. Matthew Wicker
5. Yusuke Tsuzuku
6. Xingyu Zhao
7. Min Wu
8. Marta Z. Kwiatkowska
9. Guy Katz
10. Issei Sato

Papers from Wenjie Ruan's perspective

Ordered by how strongly the researchers Wenjie Ruan 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. 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 of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability (2018) — 607 citations
• Reachability Analysis of Deep Neural Networks with Provable Guarantees (2018) — 287 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