Yanran Li

Computer Vision and Pattern Recognition · with Li · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 125 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 51 citations into it; #7,426 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 47 citations into it; #17,695 of 35,345 by trust here, #8,210 by citations
• Generative Models — 41 citations into it; #731 of 1,956 by trust here, #465 by citations
• Natural Language Processing — 34 citations into it; #3,263 of 5,688 by trust here, #1,857 by citations
• Evaluation & Benchmarks — 9 citations into it
• Model Architectures — 9 citations into it

Whom Yanran Li trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kyunghyun Cho
5. Ronald J. Williams
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Dzmitry Bahdanau
10. Jimmy Ba

Who trusts Yanran Li overall

1. Eric Michael Smith
2. Pei Zhou
3. Myle Ott
4. Hannah Rashkin
5. Sarik Ghazarian
6. Wangchunshu Zhou
7. Deepanway Ghosal
8. Yongjun Hong
9. Chen Zhang
10. Soujanya Poria

Papers from Yanran Li's perspective

Ordered by how strongly the researchers Yanran Li 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Mode Regularized Generative Adversarial Networks (2016) — 593 citations
• Maximum-Likelihood Augmented Discrete Generative Adversarial Networks (2017) — 250 citations
• DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset (2017) — 1,534 citations