Peng Ye

Computer Vision and Pattern Recognition · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 71 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 60 citations into it; #6,309 of 21,575 by trust here, #7,176 by citations
• Media Technology — 36 citations into it; #733 of 3,621 by trust here, #594 by citations
• Evaluation & Benchmarks — 12 citations into it
• Artificial Intelligence — 11 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Peng Ye trusts overall

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

1. A. Bovik
2. Anush K. Moorthy
3. Hamid R. Sheikh
4. Zhou Wang
5. Adam Coates
6. Geoffrey E. Hinton
7. Andrew Y. Ng
8. Alex Krizhevsky
9. Eric C. Larson
10. Michele A. Saad

Who trusts Peng Ye overall

1. Jongyoo Kim
2. Kede Ma
3. Weixia Zhang
4. Yochai Blau
5. Xialei Liu
6. Sanghoon Lee
7. Vlad Hosu
8. Wufeng Xue
9. S. Alireza Golestaneh
10. Sebastian Bosse

Papers from Peng Ye's perspective

Ordered by how strongly the researchers Peng Ye 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Convolutional Neural Networks for No-Reference Image Quality Assessment (2014) — 1,187 citations
• Blind Image Quality Assessment Based on High Order Statistics Aggregation (2016) — 473 citations
• Unsupervised feature learning framework for no-reference image quality assessment (2012) — 867 citations