Kaixiong Zhou

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 80 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #20,101 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Graph Learning — 35 citations into it; #465 of 693 by trust here, #606 by citations
• Reinforcement Learning — 35 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Model Architectures — 17 citations into it
• Molecular Biology — 12 citations into it
• Optimization & Training — 12 citations into it

Whom Kaixiong Zhou trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Richard S. Sutton
7. Yann LeCun
8. Jimmy Ba
9. Xavier Glorot
10. Ilya Sutskever

Who trusts Kaixiong Zhou overall

1. Yaoman Li
2. Quanming Yao
3. Yuhui Ding
4. Yi-Wei Chen
5. Huan Zhao
6. Xin He
7. Lan Wei
8. Qingquan Song
9. Irwin King
10. Yiren Zhao

Papers from Kaixiong Zhou's perspective

Ordered by how strongly the researchers Kaixiong 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Towards Deeper Graph Neural Networks with Differentiable Group Normalization (2020) — 236 citations
• Simplifying Deep Reinforcement Learning via Self-Supervision (2021) — 16 citations
• Auto-GNN: Neural architecture search of graph neural networks (2019) — 217 citations