Yang Liu

Artificial Intelligence · with Peng · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 192 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #15,708 of 35,345 by trust here, #14,181 by citations
• Molecular Biology — 89 citations into it
• Reinforcement Learning — 78 citations into it; #1,202 of 2,242 by trust here, #1,395 by citations
• Computational Biology & Bioinformatics — 56 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Model Architectures — 29 citations into it
• Natural Language Processing — 29 citations into it
• Materials Chemistry — 28 citations into it

Whom Yang Liu trusts overall

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

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

Who trusts Yang Liu overall

1. Yilun Du
2. Igor Mordatch
3. Qiang Liu
4. Yuxi Li
5. Wonseok Jeon
6. Mu Gao
7. Bhairav Mehta
8. Taesup Kim
9. Mohammed Alquraishi
10. Dilin Wang

Papers from Yang Liu's perspective

Ordered by how strongly the researchers Yang Liu 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. 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

• Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening (2016) — 87 citations
• Stein Variational Policy Gradient (2017) — 153 citations
• Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning (2019) — 41 citations
• Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017) — 113 citations
• Energy-efficient Amortized Inference with Cascaded Deep Classifiers (2017) — 41 citations
• Evolutionary context-integrated deep sequence modeling for protein engineering (2020) — 12 citations
• Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping (2018) — 26 citations