Wenbo Guo

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 206 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #35,050 of 35,345 by trust here, #12,387 by citations
• Safety, Robustness & Interpretability — 111 citations into it
• Computer Vision and Pattern Recognition — 90 citations into it
• Signal Processing — 51 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 22 citations into it
• Hardware and Architecture — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Wenbo Guo trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Karen Simonyan
4. Yann LeCun
5. Ilya Sutskever
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ian J. Goodfellow
9. Christian Szegedy
10. Kaiming He

Who trusts Wenbo Guo overall

1. Eugene Bagdasarian
2. Xijie Huang
3. Yansong Gao
4. Sebastian Szyller
5. Lixu Wang
6. Saikat Chakraborty
7. Feargus Pendlebury
8. Xiaoyu Cao
9. Vitaly Shmatikov
10. Micah Goldblum

Papers from Wenbo Guo's perspective

Ordered by how strongly the researchers Wenbo Guo 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Adversary Resistant Deep Neural Networks with an Application to Malware Detection (2016) — 181 citations
• LEMNA: Explaining Deep Learning based Security Applications (2018) — 348 citations
• TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems (2019) — 245 citations
• Using Non-invertible Data Transformations to Build Adversary-Resistant Deep Neural Networks (2016) — 10 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 4 citations
• Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks (2016) — 10 citations
• Learning Adversary-Resistant Deep Neural Networks (2016) — 44 citations