Pingkun Yan

Computer Vision and Pattern Recognition · 2012–2019

A researcher in the Science corpus: 7 papers; trusts 412 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it
• Artificial Intelligence — 79 citations into it
• Radiology, Nuclear Medicine and Imaging — 48 citations into it
• Biomedical Engineering — 37 citations into it
• Media Technology — 28 citations into it
• Model Architectures — 28 citations into it
• Generative Models — 20 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Pingkun Yan trusts overall

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

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

Who trusts Pingkun Yan overall

1. Xin Yi
2. Mufti Mahmud
3. Eun-Jae Lee
4. Xingyu Jiang
5. Yoseob Han
6. Nantheera Anantrasirichai

Papers from Pingkun Yan's perspective

Ordered by how strongly the researchers Pingkun Yan 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Deeply-supervised CNN for prostate segmentation (2017) — 217 citations
• Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017) — 1,435 citations
• Machine Learning in Medical Imaging (2012) — 906 citations
• Deep learning in medical image registration: a survey (2019) — 682 citations
• Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (2019) — 191 citations
• Learning deep similarity metric for 3D MR–TRUS image registration (2018) — 124 citations
• Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013) — 385 citations