Li-zhen Cui

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 121 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 205 citations into it; #19,686 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 70 citations into it
• Information Systems — 63 citations into it; #4,048 of 8,771 by trust here, #3,098 by citations
• Safety, Robustness & Interpretability — 34 citations into it
• Representation & Self-Supervised Learning — 33 citations into it
• Federated & Distributed Learning — 31 citations into it
• Computer Science Applications — 16 citations into it
• Graph Learning — 10 citations into it

Whom Li-zhen Cui trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Christian Szegedy
9. Ian J. Goodfellow
10. Ruslan Salakhutdinov

Who trusts Li-zhen Cui overall

1. Junliang Yu
2. Mingxia Liu

Papers from Li-zhen Cui's perspective

Ordered by how strongly the researchers Li-zhen Cui 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions

Their papers

• Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (2020) — 456 citations
• XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (2022) — 401 citations
• Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation (2021) — 990 citations
• Towards Personalized Federated Learning (2021) — 1,295 citations
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020) — 632 citations