Ruonan Li
Artificial Intelligence · 2011–2015
A researcher in the Science corpus: 4 papers; trusts 286 researchers and is trusted by 1,294 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 86 citations into it; #1,770 of 35,345 by trust here, #3,167 by citations
- Computer Vision and Pattern Recognition — 80 citations into it; #1,152 of 21,575 by trust here, #2,207 by citations
- Representation & Self-Supervised Learning — 64 citations into it; #266 of 3,145 by trust here, #456 by citations
- Computational Mechanics — 11 citations into it
- Biomedical Engineering — 8 citations into it; #1,148 of 10,734 by trust here, #6,312 by citations
- Computational Theory and Mathematics — 6 citations into it
- Statistics and Probability — 6 citations into it
- Aerospace Engineering — 5 citations into it
Whom Ruonan Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ali Farhadi
- Geoffrey E. Hinton
- Du Tran
- Alex Krizhevsky
- Yoshua Bengio
- Lotfi A. Zadeh
- Kaiming He
- David G. Lowe
- Yann LeCun
- Diederik P. Kingma
Who trusts Ruonan Li overall
- Hossein Rahmani
- Jingjing Zheng
- Judy Hoffman
- Gabriella Csurka
- Wen Li
- Boqing Gong
- Baochen Sun
- Ramalingam Chellappa
- Kate Saenko
- Ajmal Saeed Mian
Papers from Ruonan Li's perspective
Ordered by how strongly the researchers Ruonan Li 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Domain adaptation for object recognition: An unsupervised approach (2011) — 1,114 citations
- Visual Domain Adaptation: A survey of recent advances (2015) — 884 citations
- Discriminative virtual views for cross-view action recognition (2012) — 191 citations
- Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations (2014) — 213 citations