Yihui He

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 1,098 researchers and is trusted by 976 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 228 citations into it; #1,393 of 21,575 by trust here, #1,577 by citations
• Artificial Intelligence — 162 citations into it; #1,173 of 35,345 by trust here, #2,144 by citations
• Efficiency & Compression — 94 citations into it; #20 of 1,264 by trust here, #108 by citations
• Model Architectures — 43 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Optimization & Training — 23 citations into it
• Neural Architecture Search & AutoML — 16 citations into it
• Reinforcement Learning — 16 citations into it

Whom Yihui He trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Karen Simonyan
7. Shaoqing Ren
8. X. Zhang
9. Yann LeCun
10. Andrew Zisserman

Who trusts Yihui He overall

1. Yang He
2. Ji Lin
3. Jiahui Yu
4. Song Han
5. Jonathan Frankle
6. Han Cai
7. Yi Yang
8. Xuanyi Dong
9. Yunhe Wang
10. Zhijian Liu

Papers from Yihui He's perspective

Ordered by how strongly the researchers Yihui He 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Channel Pruning for Accelerating Very Deep Neural Networks (2017) — 2,781 citations
• AMC: AutoML for Model Compression and Acceleration on Mobile Devices (2018) — 1,502 citations
• Feature Selective Anchor-Free Module for Single-Shot Object Detection (2019) — 917 citations
• Bounding Box Regression With Uncertainty for Accurate Object Detection (2018) — 546 citations
• ADC: Automated Deep Compression and Acceleration with Reinforcement Learning (2018) — 77 citations