Siping Hu

Computer Vision and Pattern Recognition · 1 papers · 2017

A researcher in the Science corpus: 1 paper; trusts 13 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 7 citations into it
• Artificial Intelligence — 6 citations into it
• Model Architectures — 6 citations into it

Whom Siping Hu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton

Papers from Siping Hu's perspective

Ordered by how strongly the researchers Siping Hu 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. Going deeper with convolutions
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Dropout: a simple way to prevent neural networks from overfitting
7. Visualizing and Understanding Convolutional Networks
8. Gradient-based learning applied to document recognition
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Research on data augmentation for image classification based on convolution neural networks (2017) — 259 citations