Tianxiao Shen

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 211 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #6,698 of 35,345 by trust here, #6,064 by citations
• Natural Language Processing — 92 citations into it; #1,593 of 5,688 by trust here, #1,327 by citations
• Computer Vision and Pattern Recognition — 56 citations into it
• Generative Models — 30 citations into it
• Large Language Models — 23 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 5 citations into it

Whom Tianxiao Shen trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Graves
5. Ilya Sutskever
6. Alex Krizhevsky
7. Ronald J. Williams
8. Radford M. Neal
9. Yann LeCun
10. Jimmy Ba

Who trusts Tianxiao Shen overall

1. Chunyuan Li
2. Percy Liang
3. Guillaume Lample
4. Machel Reid
5. Mingkai Deng
6. Di Jin
7. Maruan Al-Shedivat
8. Shrimai Prabhumoye
9. Zhijing Jin
10. Nishtha Madaan

Papers from Tianxiao Shen's perspective

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

Their papers

• Style Transfer from Non-Parallel Text by Cross-Alignment (2017) — 829 citations
• Mixture Models for Diverse Machine Translation: Tricks of the Trade (2019) — 169 citations
• Blank Language Models (2020) — 77 citations
• Generating Sequences by Learning to Self-Correct (2022) — 352 citations