Ruishan Liu

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 155 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Model Architectures — 14 citations into it
• Generative Models — 13 citations into it
• Reinforcement Learning — 13 citations into it
• Efficiency & Compression — 12 citations into it
• Optimization & Training — 7 citations into it
• Signal Processing — 6 citations into it

Whom Ruishan Liu trusts overall

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

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

Who trusts Ruishan Liu overall

1. Jianping Gou
2. Suman V. Ravuri
3. Baosheng Yu
4. Oriol Vinyals
5. S. Maybank
6. Dacheng Tao
7. Shangtong Zhang
8. Aviral Kumar
9. Zhuoran Yang
10. Lin Wang

Papers from Ruishan Liu's perspective

Ordered by how strongly the researchers Ruishan 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The Effects of Memory Replay in Reinforcement Learning (2017) — 134 citations
• Evaluating eligibility criteria of oncology trials using real-world data and AI (2021) — 298 citations
• Teacher-Student Compression with Generative Adversarial Networks (2018) — 19 citations
• Model Compression with Generative Adversarial Networks (2018) — 10 citations