Yin-wei Wei

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #34,424 of 35,345 by trust here, #25,040 by citations
• Information Systems — 35 citations into it; #7,282 of 8,771 by trust here, #2,741 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Graph Learning — 12 citations into it
• Recommender Systems — 12 citations into it
• Management Science and Operations Research — 8 citations into it
• Model Architectures — 8 citations into it

Whom Yin-wei Wei trusts overall

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

1. Xiangnan He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Xiang Wang
7. Yehuda Koren
8. Xavier Glorot
9. Ruslan Salakhutdinov
10. Steffen Rendle

Who trusts Yin-wei Wei overall

1. Xin Zhou
2. Wei Wei
3. Junliang Yu
4. Zhenguang Liu
5. Junling Liu
6. Zhiqi Shen
7. Xubin Ren
8. Fei Zhao
9. Xin Xia

Papers from Yin-wei Wei's perspective

Ordered by how strongly the researchers Yin-wei Wei 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Contrastive Learning for Cold-Start Recommendation (2021) — 365 citations
• Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020) — 395 citations
• MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video (2019) — 866 citations