Tianbing Xu

Reinforcement Learning · 2014–2018

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 24 citations into it
• Artificial Intelligence — 21 citations into it
• Optimization & Training — 7 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Tianbing Xu trusts overall

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

1. J. Urgen Schmidhuber
2. Diederik P. Kingma
3. Volodymyr Mnih
4. John Schulman
5. Jimmy Ba
6. Geoffrey E. Hinton
7. Ben J. A. Kröse
8. Longxin Lin
9. Sergey Levine
10. Ian Osband

Who trusts Tianbing Xu overall

1. Rishabh Agarwal
2. Lin Lan
3. Kate Rakelly
4. Mitchell Wortsman
5. Zeyu Zheng
6. Zhenguo Li
7. Chen Liang
8. Aurick Zhou
9. Kiana Ehsani

Papers from Tianbing Xu's perspective

Ordered by how strongly the researchers Tianbing Xu 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning to Explore with Meta-Policy Gradient (2018) — 54 citations
• Practical Lessons from Predicting Clicks on Ads at Facebook (2014) — 977 citations