Chao Du

Artificial Intelligence · with Lin · 2014–2024

A researcher in the Science corpus: 8 papers; trusts 546 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 273 citations into it
• Computer Vision and Pattern Recognition — 154 citations into it; #15,185 of 21,575 by trust here, #17,258 by citations
• Safety, Robustness & Interpretability — 100 citations into it
• Signal Processing — 32 citations into it
• Optimization & Training — 31 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 24 citations into it
• Representation & Self-Supervised Learning — 24 citations into it

Whom Chao Du trusts overall

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

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

Who trusts Chao Du overall

1. Jingdong Wang
2. Tianyu Pang
3. Yinpeng Dong
4. Ting Zhang
5. Jingkuan Song

Papers from Chao Du's perspective

Ordered by how strongly the researchers Chao Du 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Improving Adversarial Robustness via Promoting Ensemble Diversity (2019) — 504 citations
• Bag of Tricks for Training Data Extraction from Language Models (2023) — 95 citations
• Locality Sensitive Sparse Encoding for Learning World Models Online (2024) — 17 citations
• LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition (2023) — 393 citations
• Better Diffusion Models Further Improve Adversarial Training (2023) — 317 citations
• On Evaluating Adversarial Robustness of Large Vision-Language Models (2023) — 369 citations
• Towards Robust Detection of Adversarial Examples (2017) — 236 citations
• Inner Product Similarity Search using Compositional Codes (2014) — 19 citations