Huan Wang

Computer Vision and Pattern Recognition · with Zhang · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 412 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it
• Artificial Intelligence — 87 citations into it
• Efficiency & Compression — 81 citations into it
• Model Architectures — 15 citations into it
• Optimization & Training — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Computational Mathematics — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Huan Wang trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Kaiming He
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Karen Simonyan
7. Andrew Zisserman
8. X. Zhang
9. Ilya Sutskever
10. Jia Deng

Who trusts Huan Wang overall

1. Yihui He
2. Gongfan Fang
3. Jose Gallego-Posada
4. Jing Liu
5. Ji Lin
6. Xinyin Ma
7. Juan Ramirez

Papers from Huan Wang's perspective

Ordered by how strongly the researchers Huan 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Structured Probabilistic Pruning for Deep Convolutional Neural Network Acceleration (2017) — 11 citations
• Neural Pruning via Growing Regularization (2020) — 193 citations
• DIME: An Information-Theoretic Difficulty Measure for AI Datasets (2019) — 13 citations
• Structured Probabilistic Pruning for Convolutional Neural Network Acceleration (2017) — 65 citations