Qiyang Li

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 3 papers; trusts 206 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #15,939 of 35,345 by trust here, #13,962 by citations
• Reinforcement Learning — 111 citations into it; #941 of 2,242 by trust here, #1,204 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Management Science and Operations Research — 24 citations into it
• Control and Systems Engineering — 20 citations into it
• Model Architectures — 18 citations into it
• Computational Theory and Mathematics — 16 citations into it
• Large Language Models — 14 citations into it

Whom Qiyang Li trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. Diederik P. Kingma
4. J. Urgen Schmidhuber
5. Geoffrey E. Hinton
6. Ronald J. Williams
7. Aviral Kumar
8. Jimmy Ba
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Qiyang Li overall

1. Gabriel Recchia
2. Yilun Du
3. Rafael Figueiredo Prudencio
4. Machel Reid
5. Vincent Micheli
6. Brandon Trabucco
7. Sai H. Vemprala
8. Nur Muhammad (Mahi) Shafiullah
9. Yueen Ma
10. David Venuto

Papers from Qiyang Li's perspective

Ordered by how strongly the researchers Qiyang Li 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Offline Reinforcement Learning as One Big Sequence Modeling Problem (2021) — 901 citations
• OpenEQA: Embodied Question Answering in the Era of Foundation Models (2024) — 348 citations
• Reinforcement Learning as One Big Sequence Modeling Problem (2021) — 93 citations