Haiyang Yu

Artificial Intelligence · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 64 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #26,362 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Model Architectures — 16 citations into it
• Materials Chemistry — 13 citations into it
• Molecular Biology — 12 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Natural Language Processing — 11 citations into it

Whom Haiyang Yu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Diederik P. Kingma
5. Sergey Ioffe
6. Alex Krizhevsky
7. Ilya Sutskever
8. Sepp Hochreiter
9. Karen Simonyan
10. Christian Szegedy

Who trusts Haiyang Yu overall

1. Zhiyong Cui
2. Ling Zhao
3. David Alexander Tedjopurnomo
4. Saeed Rahmani
5. Hao Yuan
6. Jiawei Zhu
7. Ruimin Ke
8. Yujiao Song
9. Yinhai Wang
10. Xiaolei Ma

Papers from Haiyang Yu's perspective

Ordered by how strongly the researchers Haiyang Yu 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• On Explainability of Graph Neural Networks via Subgraph Explorations (2021) — 537 citations
• Explainability in Graph Neural Networks: A Taxonomic Survey (2020) — 842 citations
• Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017) — 594 citations
• Long short-term memory neural network for traffic speed prediction using remote microwave sensor data (2015) — 2,059 citations
• Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory (2015) — 484 citations