Peng Hu

Computer Vision and Pattern Recognition · with Gong · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 42 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 49 citations into it
• Artificial Intelligence — 47 citations into it
• Efficiency & Compression — 29 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 9 citations into it

Whom Peng Hu trusts overall

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

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

Papers from Peng Hu's perspective

Ordered by how strongly the researchers Peng Hu 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction (2021) — 675 citations
• Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks (2019) — 545 citations