Xiaowei Huang

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 9 papers; trusts 207 researchers and is trusted by 144 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #4,974 of 35,345 by trust here, #5,749 by citations
• Safety, Robustness & Interpretability — 161 citations into it; #73 of 2,251 by trust here, #433 by citations
• Computer Vision and Pattern Recognition — 111 citations into it; #6,569 of 21,575 by trust here, #8,952 by citations
• Signal Processing — 28 citations into it
• Software — 25 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Electrical and Electronic Engineering — 18 citations into it; #5,891 of 16,539 by trust here, #11,641 by citations
• Program Analysis & Verification — 18 citations into it; #1 of 301 by trust here, #6 by citations

Whom Xiaowei Huang trusts overall

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

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

Who trusts Xiaowei Huang overall

1. Eric Wong
2. Wenjie Ruan
3. Guy Katz
4. J. Kolter
5. Rüdiger Ehlers
6. Alessio R. Lomuscio
7. Matthew Wicker
8. Shiqi Wang
9. Kexin Pei
10. Nina Narodytska

Papers from Xiaowei Huang's perspective

Ordered by how strongly the researchers Xiaowei Huang 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. Going deeper with convolutions

Their papers

• Safety Verification of Deep Neural Networks (2016) — 1,011 citations
• Feature-Guided Black-Box Safety Testing of Deep Neural Networks (2017) — 247 citations
• 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
• CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation (2020) — 46 citations
• A Safety Framework for Critical Systems Utilising Deep Neural Networks (2020) — 52 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
• Safety Verification of Deep Neural Networks ∗ (2017)