Chaochao Lu

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 4 papers; trusts 423 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #27,725 of 35,345 by trust here, #25,809 by citations
• Reinforcement Learning — 61 citations into it; #1,880 of 2,242 by trust here, #1,999 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #3,714 of 21,575 by trust here, #11,083 by citations
• Probabilistic & Bayesian Methods — 23 citations into it
• Causal Inference & Discovery — 22 citations into it; #295 of 364 by trust here, #315 by citations
• Management Science and Operations Research — 19 citations into it; #9,156 of 11,946 by trust here, #8,062 by citations
• Statistics and Probability — 13 citations into it; #10,300 of 12,932 by trust here, #8,279 by citations
• Signal Processing — 12 citations into it

Whom Chaochao Lu trusts overall

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

1. David G. Lowe
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Richard S. Sutton
5. Yann LeCun
6. Yoshua Bengio
7. Alex Krizhevsky
8. Sergey Levine
9. Ruslan Salakhutdinov
10. J. Urgen Schmidhuber

Who trusts Chaochao Lu overall

1. Bernhard Schölkopf
2. Yi Sun
3. Emmanuel Dupoux
4. Omkar M. Parkhi
5. Xiaogang Wang
6. Zhanpeng Zhang
7. Xiaoou Tang
8. Feng Wang
9. Seong Joon Oh
10. Andrea Vedaldi

Papers from Chaochao Lu's perspective

Ordered by how strongly the researchers Chaochao Lu 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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

• Surpassing Human-Level Face Verification Performance on LFW with GaussianFace (2014) — 342 citations
• Deconfounding Reinforcement Learning in Observational Settings (2018) — 79 citations
• Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020) — 77 citations
• AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (2021) — 77 citations