Pengzhen Ren

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 611 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 239 citations into it
• Artificial Intelligence — 230 citations into it
• Model Architectures — 53 citations into it
• Neural Architecture Search & AutoML — 49 citations into it
• Natural Language Processing — 36 citations into it
• Optimization & Training — 27 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it

Whom Pengzhen Ren trusts overall

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

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

Who trusts Pengzhen Ren overall

1. Xiaojun Chang
2. Changlin Li
3. Shen Yan

Papers from Pengzhen Ren's perspective

Ordered by how strongly the researchers Pengzhen Ren trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• A Comprehensive Survey of Scene Graphs: Generation and Application (2021) — 341 citations
• A Comprehensive Survey of Neural Architecture Search (2020) — 868 citations
• A Survey of Deep Active Learning (2020) — 1,521 citations