Chuhan Wu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 11 papers; trusts 146 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 194 citations into it; #17,311 of 35,345 by trust here, #10,798 by citations
• Information Systems — 101 citations into it; #3,481 of 8,771 by trust here, #1,740 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Natural Language Processing — 43 citations into it
• Recommender Systems — 37 citations into it
• Management Science and Operations Research — 25 citations into it
• Optimization & Training — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it

Whom Chuhan Wu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Jimmy Ba
5. Jeffrey Pennington
6. Alex Krizhevsky
7. Richard Socher
8. Ruslan Salakhutdinov
9. Ilya Sutskever
10. Yann LeCun

Who trusts Chuhan Wu overall

1. Fangzhao Wu
2. Tao Qi
3. Yongfeng Huang
4. Jialu Liu
5. Xing Xie
6. Shiwen Wu
7. Ke Zhang
8. Jin Chen
9. Tianqi Liu
10. Fajie Yuan

Papers from Chuhan Wu's perspective

Ordered by how strongly the researchers Chuhan Wu 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Neural News Recommendation with Long- and Short-term User Representations (2019) — 326 citations
• Communication-efficient federated learning via knowledge distillation (2021) — 593 citations
• A federated graph neural network framework for privacy-preserving personalization (2021) — 328 citations
• FedNER: Medical Named Entity Recognition with Federated Learning (2020) — 23 citations
• FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning (2020) — 67 citations
• FedRec: Privacy-Preserving News Recommendation with Federated Learning (2020) — 18 citations
• DA-Transformer: Distance-aware Transformer (2020) — 49 citations
• MIND: A Large-scale Dataset for News Recommendation (2020) — 670 citations
• Neural News Recommendation with Multi-Head Self-Attention (2019) — 366 citations
• NPA: Neural News Recommendation with Personalized Attention (2019) — 367 citations
• Neural News Recommendation with Attentive Multi-View Learning (2019) — 337 citations