Tao Shen

Artificial Intelligence · World wide web (Bussum) · 2017–2024

A researcher in the Science corpus: 6 papers; trusts 261 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 347 citations into it; #11,323 of 35,345 by trust here, #6,266 by citations
• Natural Language Processing — 152 citations into it; #4,685 of 5,688 by trust here, #2,307 by citations
• Computer Vision and Pattern Recognition — 105 citations into it
• Model Architectures — 41 citations into it; #1,028 of 2,278 by trust here, #652 by citations
• Representation & Self-Supervised Learning — 34 citations into it
• Optimization & Training — 30 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Tao Shen trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Richard Socher
5. Ilya Sutskever
6. Tomas Mikolov
7. Alex Krizhevsky
8. Matthew D. Zeiler
9. Yoon Kim
10. Alex Graves

Who trusts Tao Shen overall

1. Michael Hahn
2. Tianyi Zhou
3. Baosong Yang
4. Qian Chen
5. Dichao Hu
6. Guodong Long
7. Yi Tay
8. Mohammed Hassanin
9. Gianni Brauwers
10. Qipeng Guo

Papers from Tao Shen's perspective

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

Their papers

• Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling (2018) — 148 citations
• DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (2017) — 787 citations
• Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling (2018) — 153 citations
• A Survey on Knowledge Distillation of Large Language Models (2024) — 355 citations
• Multi-center federated learning: clients clustering for better personalization (2020) — 379 citations
• Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion (2020) — 334 citations