Tao Qi

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 49 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #25,687 of 35,345 by trust here, #17,412 by citations
• Information Systems — 34 citations into it; #5,826 of 8,771 by trust here, #3,640 by citations
• Natural Language Processing — 26 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Recommender Systems — 13 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Management Science and Operations Research — 8 citations into it
• Model Architectures — 7 citations into it

Whom Tao Qi trusts overall

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

1. Chuhan Wu
2. Steffen Rendle
3. Fangzhao Wu
4. Yoshua Bengio
5. John C. Duchi
6. Mingxiao An
7. Kyunghyun Cho
8. Geoffrey E. Hinton
9. H. Brendan McMahan
10. Diederik P. Kingma

Who trusts Tao Qi overall

1. Chuhan Wu
2. Fangzhao Wu

Papers from Tao Qi's perspective

Ordered by how strongly the researchers Tao Qi 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• 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
• MIND: A Large-scale Dataset for News Recommendation (2020) — 670 citations
• Neural News Recommendation with Multi-Head Self-Attention (2019) — 366 citations