Songyang Lao

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 787 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 200 citations into it; #14,149 of 21,575 by trust here, #6,281 by citations
• Artificial Intelligence — 80 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 17 citations into it
• Aerospace Engineering — 13 citations into it
• Generative Models — 12 citations into it
• Media Technology — 11 citations into it

Whom Songyang Lao trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Kaiming He
5. Yann LeCun
6. Ross B. Girshick
7. Ruslan Salakhutdinov
8. Andrew Zisserman
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Songyang Lao overall

1. Anne Humeau-Heurtier
2. German Ignacio Parisi
3. Yanming Guo

Papers from Songyang Lao's perspective

Ordered by how strongly the researchers Songyang Lao 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Median Robust Extended Local Binary Pattern for Texture Classification (2016) — 421 citations
• Adaptive Triplet Model for Fine-Grained Visual Categorization (2018) — 5 citations
• Deep learning for visual understanding: A review (2016) — 2,298 citations