Binbin Hu

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #13,760 of 35,345 by trust here, #12,761 by citations
• Information Systems — 42 citations into it; #2,575 of 8,771 by trust here, #2,203 by citations
• Statistical and Nonlinear Physics — 36 citations into it
• Graph Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Recommender Systems — 19 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Signal Processing — 10 citations into it

Whom Binbin Hu trusts overall

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

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

Who trusts Binbin Hu overall

1. Walid Krichene
2. Steffen Rendle
3. Yuping Zhang
4. Aminu Da’u
5. Qingyu Guo
6. Yuanfu Lu
7. Xiao Wang
8. Chuan Shi
9. Shuai Zhang
10. Yuhao Yang

Papers from Binbin Hu's perspective

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

Their papers

• Heterogeneous Graph Structure Learning for Graph Neural Networks (2021) — 319 citations
• Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018) — 706 citations
• Heterogeneous Information Network Embedding for Recommendation (2017) — 1,030 citations