Tianlin Shi

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 144 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #12,215 of 35,345 by trust here, #4,466 by citations
• Reinforcement Learning — 50 citations into it; #1,994 of 2,242 by trust here, #1,519 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #11,705 of 21,575 by trust here, #3,875 by citations
• Natural Language Processing — 41 citations into it; #4,417 of 5,688 by trust here, #1,300 by citations
• Generative Models — 21 citations into it
• Management Science and Operations Research — 11 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 11 citations into it

Whom Tianlin Shi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. Alex Krizhevsky
7. Richard S. Sutton
8. Ronald J. Williams
9. Alex Graves
10. Kaiming He

Who trusts Tianlin Shi overall

1. Izzeddin Gur
2. Christopher Rawles
3. Panupong Pasupat
4. Geunwoo Kim
5. Evan Zheran Liu
6. Boyuan Zheng
7. Shuyan Zhou
8. Jing Yu Koh
9. Natasha Jaques
10. Peter Conway Humphreys

Papers from Tianlin Shi's perspective

Ordered by how strongly the researchers Tianlin Shi 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Adversarial Learning for Neural Dialogue Generation (2017) — 918 citations
• Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration (2018) — 349 citations
• Max-Margin Deep Generative Models (2015) — 34 citations
• World of Bits: An Open-Domain Platform for Web-Based Agents (2017) — 341 citations