Shuai Tang

Artificial Intelligence · with Sa · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 417 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it
• Natural Language Processing — 94 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Representation & Self-Supervised Learning — 38 citations into it
• Model Architectures — 31 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Optimization & Training — 9 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Shuai Tang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Tomas Mikolov
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Jimmy Ba
9. Richard Socher
10. Quoc V. Le

Who trusts Shuai Tang overall

1. Alexis Conneau
2. Alex Wang
3. Najoung Kim
4. Samuel R. Bowman
5. R. Thomas McCoy
6. Roma Patel

Papers from Shuai Tang's perspective

Ordered by how strongly the researchers Shuai Tang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Rethinking Skip-thought: A Neighborhood based Approach (2017) — 17 citations
• What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks (2015) — 6 citations
• A Simple Recurrent Unit with Reduced Tensor Product Representations (2018)
• Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations (2018) — 4 citations
• Trimming and Improving Skip-thought Vectors (2017) — 7 citations