Lin Wang
Computer Vision and Pattern Recognition · with Yoon · 2020
A researcher in the Science corpus: 1 paper; trusts 1,058 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 166 citations into it; #11,453 of 21,575 by trust here, #15,162 by citations
- Artificial Intelligence — 156 citations into it; #18,192 of 35,345 by trust here, #23,077 by citations
- Efficiency & Compression — 47 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
- Generative Models — 12 citations into it
- Optimization & Training — 12 citations into it
- Radiology, Nuclear Medicine and Imaging — 12 citations into it
- Meta & Continual Learning — 10 citations into it
Whom Lin Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Diederik P. Kingma
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
- Christian Szegedy
- Karen Simonyan
Who trusts Lin Wang overall
- Mozhgan Pourkeshavarz
- Md Maruf Hossain Shuvo
- Liangchen Luo
- Minrui Xu
- Guoying Zhao
- Syed Kamrul Islam
- Mark Sandler
- Zhiqiang Shen
- Manish Gupta
- Mohammad Sabokrou
Papers from Lin Wang's perspective
Ordered by how strongly the researchers Lin Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge