Hongzhi Yin

Artificial Intelligence · 2012–2022

A researcher in the Science corpus: 13 papers; trusts 369 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 315 citations into it; #9,993 of 35,345 by trust here, #9,641 by citations
• Information Systems — 189 citations into it; #1,495 of 8,771 by trust here, #1,118 by citations
• Computer Vision and Pattern Recognition — 149 citations into it
• Representation & Self-Supervised Learning — 78 citations into it
• Management Science and Operations Research — 53 citations into it
• Natural Language Processing — 38 citations into it
• Model Architectures — 37 citations into it
• Recommender Systems — 36 citations into it

Whom Hongzhi Yin trusts overall

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

1. Yehuda Koren
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Yann LeCun
9. Alex Graves
10. Ruslan Salakhutdinov

Who trusts Hongzhi Yin overall

1. Junliang Yu
2. Charu C. Aggarwal
3. Xin Xia
4. Jia-Dong Zhang
5. Himan Abdollahpouri
6. Tong Chen
7. Lianghao Xia
8. Xubin Ren
9. Ruihong Qiu
10. Chen Gao

Papers from Hongzhi Yin's perspective

Ordered by how strongly the researchers Hongzhi Yin 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Survey of Machine Unlearning (2022) — 439 citations
• Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017) — 323 citations
• Challenging the Long Tail Recommendation (2012) — 366 citations
• 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
• Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (2021) — 572 citations
• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021) — 617 citations
• Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020) — 428 citations
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020) — 632 citations
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019) — 417 citations
• Learning Graph-based POI Embedding for Location-based Recommendation (2016) — 373 citations
• LCARS: a location-content-aware recommender system (2013) — 377 citations