Dongxian Wu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 255 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #26,626 of 35,345 by trust here, #23,077 by citations
• Safety, Robustness & Interpretability — 71 citations into it; #763 of 2,251 by trust here, #1,290 by citations
• Computer Vision and Pattern Recognition — 51 citations into it
• Signal Processing — 24 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 18 citations into it
• Electrical and Electronic Engineering — 11 citations into it
• Hardware and Architecture — 9 citations into it

Whom Dongxian Wu trusts overall

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

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

Who trusts Dongxian Wu overall

1. Sylvestre-Alvise Rebuffi
2. Sven Gowal
3. Yiming Li
4. Matthew Norton
5. Yige Li
6. David Stutz
7. Anqi Mao
8. Xingjun Ma
9. Yaowei Zheng
10. Nicholas Carlini

Papers from Dongxian Wu's perspective

Ordered by how strongly the researchers Dongxian 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets (2020) — 364 citations
• Adversarial Neuron Pruning Purifies Backdoored Deep Models (2021) — 410 citations
• Adversarial Weight Perturbation Helps Robust Generalization (2020) — 894 citations