Kai Y. Xiao

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 94 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #16,545 of 35,345 by trust here, #13,760 by citations
• Safety, Robustness & Interpretability — 57 citations into it; #417 of 2,251 by trust here, #930 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #16,561 of 21,575 by trust here, #15,162 by citations
• Electrical and Electronic Engineering — 9 citations into it
• Hardware and Architecture — 9 citations into it
• Program Analysis & Verification — 9 citations into it; #149 of 301 by trust here, #99 by citations
• Optimization & Training — 8 citations into it
• Model Architectures — 5 citations into it

Whom Kai Y. Xiao trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Nicholas Carlini
5. Christian Szegedy
6. Kaiming He
7. Rüdiger Ehlers
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Kai Y. Xiao overall

1. Eric Wong
2. Francesco Croce
3. Hadi Salman
4. Matt Jordan
5. Leslie Rice
6. Sven Gowal
7. J. Kolter
8. Axel Sauer
9. Sayak Paul
10. Maksym Andriushchenko

Papers from Kai Y. Xiao's perspective

Ordered by how strongly the researchers Kai Y. Xiao 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Noise or Signal: The Role of Image Backgrounds in Object Recognition (2020) — 466 citations
• Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability (2018) — 211 citations
• Evaluating Robustness of Neural Networks with Mixed Integer Programming (2017) — 967 citations