Junliang Yu

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 98 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #15,106 of 35,345 by trust here, #13,962 by citations
• Information Systems — 111 citations into it; #2,898 of 8,771 by trust here, #2,361 by citations
• Computer Vision and Pattern Recognition — 80 citations into it
• Representation & Self-Supervised Learning — 52 citations into it
• Graph Learning — 19 citations into it

Whom Junliang Yu trusts overall

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

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

Who trusts Junliang Yu overall

1. Hongzhi Yin
2. Xin Xia
3. Lianghao Xia
4. Xubin Ren
5. Chao Huang
6. Tong Chen
7. Xuheng Cai
8. Ruihong Qiu
9. Yuhao Yang
10. Wei Wei

Papers from Junliang Yu's perspective

Ordered by how strongly the researchers Junliang Yu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Self-Supervised Learning for Recommender Systems: A Survey (2022) — 438 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
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021) — 617 citations
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020) — 632 citations