Yuhang Li

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 149 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #16,255 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #10,357 of 21,575 by trust here, #12,992 by citations
• Efficiency & Compression — 28 citations into it
• Optimization & Training — 22 citations into it
• Electrical and Electronic Engineering — 18 citations into it
• Cognitive Neuroscience — 12 citations into it
• Model Architectures — 8 citations into it

Whom Yuhang Li trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. James Martens
7. Yann LeCun
8. Shaoqing Ren
9. Ross B. Girshick
10. X. Zhang

Who trusts Yuhang Li overall

1. Jing Liu
2. Zechun Liu
3. Minsik Cho
4. Elias Frantar
5. Jianfei Cai
6. Bohan Zhuang
7. Jerry Chee
8. Ji Lin
9. Itay Hubara
10. Keivan Alizadeh-Vahid

Papers from Yuhang Li's perspective

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

Their papers

• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction (2021) — 675 citations
• Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks (2019) — 334 citations
• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting (2022) — 383 citations