Evan Zheran Liu

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 135 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #9,873 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 59 citations into it; #2,267 of 5,688 by trust here, #2,422 by citations
• Reinforcement Learning — 51 citations into it; #1,005 of 2,242 by trust here, #827 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #9,332 of 21,575 by trust here, #8,409 by citations
• Management Science and Operations Research — 17 citations into it
• Optimization & Training — 15 citations into it
• Safety, Robustness & Interpretability — 14 citations into it
• Model Architectures — 13 citations into it

Whom Evan Zheran Liu trusts overall

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

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

Who trusts Evan Zheran Liu overall

1. Izzeddin Gur
2. Geunwoo Kim
3. Sheng Jia
4. Christopher Rawles
5. Shunyu Yao
6. Panupong Pasupat
7. Sahisnu Mazumder
8. Polina Kirichenko
9. Elliot Creager
10. Shuyan Zhou

Papers from Evan Zheran Liu's perspective

Ordered by how strongly the researchers Evan Zheran Liu 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood (2017) — 199 citations
• Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration (2018) — 349 citations
• Just Train Twice: Improving Group Robustness without Training Group Information (2021) — 724 citations
• Mapping natural language commands to web elements (2018) — 52 citations