Jonathan Long

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 860 researchers and is trusted by 16,896 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it; #50 of 21,575 by trust here, #71 by citations
• Artificial Intelligence — 48 citations into it; #139 of 35,345 by trust here, #215 by citations
• Aerospace Engineering — 13 citations into it
• Model Architectures — 11 citations into it; #122 of 2,278 by trust here, #54 by citations
• Evaluation & Benchmarks — 9 citations into it
• Biomedical Engineering — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Human-Computer Interaction — 7 citations into it

Whom Jonathan Long trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. David G. Lowe
6. Pedro F. Felzenszwalb
7. Kaiming He
8. Jitendra Malik
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Jonathan Long overall

1. Kaiming He
2. Liang-Chieh Chen
3. Trevor Darrell
4. Ross B. Girshick
5. Xiaodan Liang
6. Alan L. Yuille
7. Chunhua Shen
8. Luc Van Gool
9. Jitendra Malik
10. Abhinav Kumar Gupta

Papers from Jonathan Long's perspective

Ordered by how strongly the researchers Jonathan Long 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Fully convolutional networks for semantic segmentation (2014) — 42,416 citations
• Caffe: Convolutional Architecture for Fast Feature Embedding (2014) — 14,879 citations
• Fully Convolutional Multi-Class Multiple Instance Learning (2014) — 326 citations
• Do Convnets Learn Correspondence? (2014) — 316 citations
• Fully Convolutional Networks for Semantic Segmentation (2017) — 1,912 citations