Yongfeng Huang

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 9 papers; trusts 147 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #12,403 of 35,345 by trust here, #13,760 by citations
• Information Systems — 77 citations into it; #3,895 of 8,771 by trust here, #3,012 by citations
• Computer Vision and Pattern Recognition — 36 citations into it
• Natural Language Processing — 36 citations into it
• Recommender Systems — 25 citations into it
• Optimization & Training — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Management Science and Operations Research — 17 citations into it

Whom Yongfeng Huang 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. Robert Tibshirani
6. Alex Krizhevsky
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Richard Socher
10. Yann LeCun

Who trusts Yongfeng Huang overall

1. Yu Zhang
2. Xilun Chen
3. Chuhan Wu
4. Fangzhao Wu
5. Qiang Yang
6. Tao Qi
7. Claire Cardie

Papers from Yongfeng Huang's perspective

Ordered by how strongly the researchers Yongfeng Huang 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. Neural Machine Translation by Jointly Learning to Align and Translate (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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• 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
• 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
• Collaborative Multi-domain Sentiment Classification (2015) — 57 citations