Dongruo Zhou

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 105 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #10,137 of 35,345 by trust here, #8,283 by citations
• Computer Vision and Pattern Recognition — 45 citations into it
• Optimization & Training — 39 citations into it; #1,071 of 2,601 by trust here, #804 by citations
• Management Science and Operations Research — 35 citations into it
• Reinforcement Learning — 32 citations into it
• Scaling & Learning Theory — 32 citations into it
• Statistical Learning Theory — 23 citations into it
• Model Architectures — 16 citations into it

Whom Dongruo Zhou trusts overall

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

1. Zeyuan Allen-Zhu
2. Yuanzhi Li
3. Peter Auer
4. Simon Shaolei Du
5. Yann LeCun
6. Lihong Li
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Sanjeev Arora
10. Alex Krizhevsky

Who trusts Dongruo Zhou overall

1. Quanquan Gu
2. Simon Shaolei Du
3. Greg Yang
4. Yuan Cao
5. Yuanbin Cao
6. Sanjeev Arora
7. Cong Fang
8. Alberto Bietti
9. Samet Oymak
10. Lili Su

Papers from Dongruo Zhou's perspective

Ordered by how strongly the researchers Dongruo Zhou 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks (2018) — 454 citations
• Gradient descent optimizes over-parameterized deep ReLU networks (2019) — 290 citations
• Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation (2021) — 36 citations
• Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL (2021) — 13 citations
• Neural Contextual Bandits with UCB-based Exploration (2019) — 342 citations