Ruihong Qiu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 149 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #23,808 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Information Systems — 31 citations into it; #5,514 of 8,771 by trust here, #5,078 by citations
• Model Architectures — 14 citations into it
• Management Science and Operations Research — 13 citations into it
• Graph Learning — 10 citations into it
• Recommender Systems — 9 citations into it

Whom Ruihong Qiu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Oriol Vinyals
7. Ilya Sutskever
8. Yann LeCun
9. Sepp Hochreiter
10. Balázs Hidasi

Who trusts Ruihong Qiu overall

1. Junliang Yu
2. Tianwen Chen
3. Hongzhi Yin
4. Shoujin Wang
5. Ziyang Wang
6. Chen Gao
7. Xin Xia
8. Xu Xie
9. Zi-Liang Huang
10. Raymond Chi-Wing Wong

Papers from Ruihong Qiu's perspective

Ordered by how strongly the researchers Ruihong Qiu 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. 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

• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (2021) — 572 citations
• Learning to Diversify for Single Domain Generalization (2021) — 353 citations
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019) — 417 citations