Pan Ji

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 224 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it; #9,646 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 32 citations into it; #14,975 of 35,345 by trust here, #12,761 by citations
• Generative Models — 10 citations into it; #906 of 1,956 by trust here, #1,010 by citations
• Meta & Continual Learning — 10 citations into it; #458 of 1,041 by trust here, #443 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Mechanics — 8 citations into it
• Optimization & Training — 8 citations into it
• Model Architectures — 7 citations into it

Whom Pan Ji trusts overall

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

1. Geoffrey E. Hinton
2. René Vidal
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ehsan Elhamifar
6. Yoshua Bengio
7. Yann LeCun
8. Kaiming He
9. Ilya Sutskever
10. Andrew Y. Ng

Who trusts Pan Ji overall

1. Poojan Oza
2. Xu Yang
3. Jiangpeng He
4. Zhiyuan Dang
5. José Lezama
6. Cheng Deng
7. Hongjoon Ahn
8. Jianlong Wu
9. Changqing Zhang
10. Vishal M. Patel

Papers from Pan Ji's perspective

Ordered by how strongly the researchers Pan Ji 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

• Incremental Learning Using Conditional Adversarial Networks (2019) — 193 citations
• Deep Subspace Clustering Networks (2017) — 586 citations