Qi Cai

Artificial Intelligence · with Wang · 2019–2022

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #27,728 of 35,345 by trust here, #24,417 by citations
• Reinforcement Learning — 37 citations into it
• Management Science and Operations Research — 22 citations into it; #9,132 of 11,946 by trust here, #6,888 by citations
• Computational Theory and Mathematics — 8 citations into it
• Optimization & Training — 7 citations into it
• Computer Networks and Communications — 6 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Qi Cai trusts overall

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

1. Sham M. Kakade
2. Lin F. Yang
3. Richard S. Sutton
4. Ian Osband
5. Christopher K. I. Williams
6. Chi Jin
7. Andrew Barto
8. John Schulman
9. Justin A. Boyan
10. Mengdi Wang

Who trusts Qi Cai overall

1. Ruosong Wang
2. Qiaomin Xie
3. Zhuoran Yang
4. Weitong Zhang
5. Tengyang Xie
6. Wei Xiong
7. Zhaoran Wang
8. Chi Jin
9. Simon Shaolei Du
10. Alekh Agarwal

Papers from Qi Cai's perspective

Ordered by how strongly the researchers Qi Cai 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Reinforcement Learning: An Introduction
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

• Provably Efficient Exploration in Policy Optimization (2019) — 306 citations
• Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations (2022) — 9 citations