Lina Yao

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 167 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #17,091 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 123 citations into it; #10,849 of 21,575 by trust here, #8,612 by citations
• Information Systems — 95 citations into it; #3,465 of 8,771 by trust here, #2,821 by citations
• Management Science and Operations Research — 44 citations into it
• Signal Processing — 43 citations into it
• Graph Learning — 38 citations into it
• Representation & Self-Supervised Learning — 37 citations into it
• Recommender Systems — 31 citations into it

Whom Lina Yao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Kaiming He
5. Ronald J. Williams
6. Alex Krizhevsky
7. Yann LeCun
8. Hugo Larochelle
9. Ilya Sutskever
10. Ruslan Salakhutdinov

Who trusts Lina Yao overall

1. Jeongwhan Choi
2. Yi Tay
3. Yongfeng Zhang
4. P. Li
5. Guangyuan Piao
6. Srijan Kumar
7. Zezhi Shao
8. Saeed Rahmani
9. Chao Song
10. Hwangyong Choi

Papers from Lina Yao's perspective

Ordered by how strongly the researchers Lina Yao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Quaternion Knowledge Graph Embeddings (2019) — 583 citations
• Adversarially Regularized Graph Autoencoder (2018) — 489 citations
• A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020) — 300 citations
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020) — 2,016 citations
• Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020) — 326 citations
• Deep Learning for Sensor-based Human Activity Recognition (2020) — 862 citations
• STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting (2019) — 308 citations
• Deep Learning Based Recommender System (2017) — 1,327 citations