Ruining He

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 309 researchers and is trusted by 172 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #3,800 of 35,345 by trust here, #4,231 by citations
• Information Systems — 75 citations into it; #90 of 8,771 by trust here, #137 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #2,900 of 21,575 by trust here, #3,692 by citations
• Recommender Systems — 51 citations into it; #32 of 233 by trust here, #28 by citations
• Management Science and Operations Research — 33 citations into it; #1,893 of 11,946 by trust here, #4,267 by citations
• Graph Learning — 29 citations into it; #285 of 693 by trust here, #152 by citations
• Model Architectures — 21 citations into it
• Marketing — 20 citations into it; #138 of 6,289 by trust here, #603 by citations

Whom Ruining He trusts overall

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

1. Yehuda Koren
2. Julian McAuley
3. Steffen Rendle
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Robert M. Bell
7. Ruslan Salakhutdinov
8. Ilya Sutskever
9. Kaiming He
10. Yoshua Bengio

Who trusts Ruining He overall

1. Xu Chen
2. Yongfeng Zhang
3. Wang-Cheng Kang
4. Hu Xu
5. Xiang Wang
6. Julian McAuley
7. Jiacheng Li
8. Xiangnan He
9. Qingyao Ai
10. Jiaxi Tang

Papers from Ruining He's perspective

Ordered by how strongly the researchers Ruining He 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016) — 2,362 citations
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems (2018) — 4,201 citations
• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015) — 1,225 citations
• Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation (2016) — 882 citations
• RealFormer: Transformer Likes Residual Attention (2020) — 138 citations
• Translation-based Recommendation (2017) — 477 citations