Jian-Hao Luo

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 837 researchers and is trusted by 185 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it; #2,694 of 21,575 by trust here, #3,598 by citations
• Artificial Intelligence — 64 citations into it; #2,959 of 35,345 by trust here, #5,120 by citations
• Efficiency & Compression — 30 citations into it; #101 of 1,264 by trust here, #293 by citations
• Model Architectures — 22 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Media Technology — 12 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Jian-Hao Luo trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Karen Simonyan
5. Ross B. Girshick
6. Yann LeCun
7. Ilya Sutskever
8. Yoshua Bengio
9. Andrew Zisserman
10. Christian Szegedy

Who trusts Jian-Hao Luo overall

1. Yang He
2. Jonathan Frankle
3. Charles Herrmann
4. Yunhe Wang
5. Kai Han
6. Deepak Mittal
7. Inyoung Paik
8. Shaohui Lin
9. Frederick Tung
10. Georgios Georgiadis

Papers from Jian-Hao Luo's perspective

Ordered by how strongly the researchers Jian-Hao Luo 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. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression (2017) — 1,876 citations
• Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval (2016) — 442 citations
• In Defense of Fully Connected Layers in Visual Representation Transfer (2017) — 85 citations
• An Entropy-based Pruning Method for CNN Compression (2017) — 206 citations