Guangneng Hu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 100 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #16,148 of 35,345 by trust here, #24,417 by citations
• Information Systems — 50 citations into it; #3,146 of 8,771 by trust here, #5,501 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #10,317 of 21,575 by trust here, #15,955 by citations
• Recommender Systems — 26 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Management Science and Operations Research — 13 citations into it
• Natural Language Processing — 10 citations into it
• Marketing — 5 citations into it

Whom Guangneng Hu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Julian McAuley
4. Steffen Rendle
5. Alex Krizhevsky
6. Ruslan Salakhutdinov
7. Yoshua Bengio
8. Yehuda Koren
9. Robert Tibshirani
10. Ajit Paul Singh

Who trusts Guangneng Hu overall

1. P. Li
2. Qian Zhang
3. Yuanfu Lu
4. Alexander Tuzhilin
5. Jie Lu
6. Yu Zhang
7. Yuan Fang
8. Fajie Yuan
9. Garrett Wilson

Papers from Guangneng Hu's perspective

Ordered by how strongly the researchers Guangneng Hu 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019) — 85 citations
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (2018) — 492 citations