Shawn Tan

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #25,013 of 35,345 by trust here, #15,542 by citations
• Natural Language Processing — 29 citations into it
• Model Architectures — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #16,633 of 21,575 by trust here, #12,419 by citations
• Signal Processing — 7 citations into it; #7,451 of 7,572 by trust here, #5,570 by citations
• Evaluation & Benchmarks — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Shawn Tan trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Matthew D. Zeiler
4. Yann LeCun
5. A. Goh
6. Richard Socher
7. Alex Krizhevsky
8. Abdel-rahman Mohamed
9. Diederik P. Kingma
10. L. V. D. van der Maaten

Who trusts Shawn Tan overall

1. Chunyang Wu
2. Mark John Francis Gales
3. Yoon Kim
4. Yanpeng Zhao
5. Panagiota Karanasou
6. Freda Shi
7. Zhiyong Wu
8. Yu Wang
9. Amina Adadi
10. Seyedmahdad Mirsamadi

Papers from Shawn Tan's perspective

Ordered by how strongly the researchers Shawn Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (2018) — 349 citations
• Improving the interpretability of deep neural networks with stimulated learning (2015) — 47 citations