Yanyao Shen
Artificial Intelligence · 2017–2018
A researcher in the Science corpus: 3 papers; trusts 129 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 64 citations into it; #31,654 of 35,345 by trust here, #17,412 by citations
- Computer Vision and Pattern Recognition — 31 citations into it; #19,642 of 21,575 by trust here, #17,757 by citations
- Natural Language Processing — 28 citations into it; #5,136 of 5,688 by trust here, #4,879 by citations
- Optimization & Training — 15 citations into it; #1,268 of 2,601 by trust here, #1,744 by citations
- Model Architectures — 14 citations into it
- Safety, Robustness & Interpretability — 10 citations into it; #901 of 2,251 by trust here, #1,673 by citations
- Statistics and Probability — 9 citations into it
- Signal Processing — 8 citations into it
Whom Yanyao Shen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Kaiming He
- Diederik P. Kingma
- Dzmitry Bahdanau
- Alex Graves
- Kyunghyun Cho
Who trusts Yanyao Shen overall
- Xu Tan
- Kawin Ethayarajh
- Swabha Swayamdipta
- Suyu Ge
- Hwanjun Song
- Abhijeet Awasthi
- Robert Munro
- Yejin Choi
- Guotai Wang
- Bill Yuchen Lin
Papers from Yanyao Shen's perspective
Ordered by how strongly the researchers Yanyao Shen 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep Active Learning for Named Entity Recognition (2017) — 482 citations
- Learning with Bad Training Data via Iterative Trimmed Loss Minimization (2018) — 297 citations
- Dense Information Flow for Neural Machine Translation (2018) — 36 citations