Chen Dan

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 63 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it
• Safety, Robustness & Interpretability — 38 citations into it
• Statistics and Probability — 30 citations into it
• Management Science and Operations Research — 13 citations into it
• Statistical Learning Theory — 12 citations into it
• Causal Inference & Discovery — 11 citations into it
• Signal Processing — 9 citations into it

Whom Chen Dan trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Jan Peters
4. Alex Krizhevsky
5. Yoshua Bengio
6. Bernhard Schölkopf
7. Yann LeCun
8. Diederik P. Kingma
9. John C. Duchi
10. Tom L. Burr

Who trusts Chen Dan overall

1. Matthew James Vowels
2. Gonçalo Faria
3. Ignavier Ng
4. Lars Lorch

Papers from Chen Dan's perspective

Ordered by how strongly the researchers Chen Dan 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Adversarially Robust Generalization Just Requires More Unlabeled Data (2019) — 161 citations
• Learning Sparse Nonparametric DAGs (2019) — 339 citations
• Class-Weighted Classification: Trade-offs and Robust Approaches (2020) — 56 citations
• DORO: Distributional and Outlier Robust Optimization (2021) — 75 citations