Chao-Yuan Wu
Computer Vision and Pattern Recognition · 2014–2018
A researcher in the Science corpus: 4 papers; trusts 522 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 106 citations into it; #4,168 of 21,575 by trust here, #6,031 by citations
- Artificial Intelligence — 65 citations into it; #25,628 of 35,345 by trust here, #10,207 by citations
- Model Architectures — 15 citations into it
- Evaluation & Benchmarks — 13 citations into it
- Optimization & Training — 12 citations into it
- Information Systems — 9 citations into it; #5,829 of 8,771 by trust here, #2,145 by citations
- Generative Models — 8 citations into it
- Endocrinology, Diabetes and Metabolism — 6 citations into it
Whom Chao-Yuan Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Karen Simonyan
- Ross B. Girshick
- Alex Krizhevsky
- Andrew Zisserman
- Yoshua Bengio
- Yann LeCun
- Jimmy Ba
Who trusts Chao-Yuan Wu overall
- Christoph Feichtenhofer
- Bruno Korbar
- Chen Sun
- Guo Lu
- Eirikur Agustsson
- Kristen Grauman
- Anurag Arnab
- Rohit Girdhar
- Michael Tschannen
- Haoqi Fan
Papers from Chao-Yuan Wu's perspective
Ordered by how strongly the researchers Chao-Yuan Wu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Long-Term Feature Banks for Detailed Video Understanding (2018) — 526 citations
- Compressed Video Action Recognition (2017) — 371 citations
- Video Compression through Image Interpolation (2018) — 352 citations
- Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014) — 461 citations