Kuan Wang

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 279 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #20,834 of 35,345 by trust here, #8,893 by citations
• Computer Vision and Pattern Recognition — 99 citations into it; #12,948 of 21,575 by trust here, #6,954 by citations
• Efficiency & Compression — 52 citations into it; #944 of 1,264 by trust here, #603 by citations
• Model Architectures — 26 citations into it
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Kuan Wang trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. François Chollet
7. Song Han
8. Jia Deng
9. Yann LeCun
10. Ilya Sutskever

Who trusts Kuan Wang overall

1. Ji Lin
2. Zhen Dong
3. Zi-Mei Yao
4. Nimit Sharad Sohoni
5. Song Han
6. Zhijian Liu
7. Ruihao Gong
8. Qing Jin
9. Haotian Tang
10. Jing Liu

Papers from Kuan Wang's perspective

Ordered by how strongly the researchers Kuan Wang 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• HAQ: Hardware-Aware Automated Quantization With Mixed Precision (2018) — 1,120 citations
• Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks (2018) — 93 citations
• GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning (2020) — 351 citations
• ToolQA: A Dataset for LLM Question Answering with External Tools (2023) — 396 citations
• HAQ: Hardware-Aware Automated Quantization (2018) — 101 citations