Li Zhao

Artificial Intelligence · with Liu · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 90 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #7,316 of 35,345 by trust here, #5,720 by citations
• Natural Language Processing — 73 citations into it; #1,650 of 5,688 by trust here, #1,504 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #19,387 of 21,575 by trust here, #9,187 by citations
• Reinforcement Learning — 33 citations into it
• Model Architectures — 24 citations into it
• Optimization & Training — 11 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Li Zhao trusts overall

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

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

Who trusts Li Zhao overall

1. Xu Han
2. Lijun Wu
3. Binxuan Huang
4. Xin Li
5. Shuai Wang
6. Tom Young
7. Nino Vieillard
8. Tianxing He

Papers from Li Zhao's perspective

Ordered by how strongly the researchers Li Zhao 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Adversarial Neural Machine Translation (2017) — 137 citations
• Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization (2018) — 66 citations
• Attention-based LSTM for Aspect-level Sentiment Classification (2016) — 2,183 citations
• Reinforcement Learning for Relation Classification From Noisy Data (2018) — 353 citations
• Fully Parameterized Quantile Function for Distributional Reinforcement Learning (2019) — 183 citations