Huangjing Lin

Computer Vision and Pattern Recognition · 2017–2020

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

Subfields

• Computer Vision and Pattern Recognition — 60 citations into it

Whom Huangjing Lin trusts

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

1. Andrew Zisserman (in Computer Vision and Pattern Recognition)
2. Karen Simonyan (in Computer Vision and Pattern Recognition)
3. Jaume Amores (in Computer Vision and Pattern Recognition)

Papers from Huangjing Lin's perspective

Ordered by how strongly the researchers Huangjing Lin 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Going deeper with convolutions
4. ImageNet Large Scale Visual Recognition Challenge
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Dropout: a simple way to prevent neural networks from overfitting
8. Rectified Linear Units Improve Restricted Boltzmann Machines
9. Understanding the difficulty of training deep feedforward neural networks
10. Rethinking the Inception Architecture for Computer Vision

Their papers

• Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 citations
• Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020) — 373 citations
• From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge (2019) — 709 citations