Yujun Yan

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it
• Graph Learning — 42 citations into it
• Statistical and Nonlinear Physics — 30 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Model Architectures — 17 citations into it
• Information Systems — 16 citations into it
• Natural Language Processing — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Yujun Yan trusts overall

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

1. Ilya Sutskever
2. Geoffrey E. Hinton
3. Thomas Kipf
4. Yoshua Bengio
5. Max Welling
6. Diederik P. Kingma
7. Tomas Mikolov
8. Alex Krizhevsky
9. Alex Graves
10. Michaël Defferrard

Who trusts Yujun Yan overall

1. Gwonsoo Che
2. Oleg Platonov
3. Erico Tjoa
4. Xiang Li
5. Ribana Roscher
6. Derek Lim
7. Hattie Zhou
8. Jiong Zhu
9. Hongseok Yang
10. Denis Kuznedelev

Papers from Yujun Yan's perspective

Ordered by how strongly the researchers Yujun Yan 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural Execution Engines: Learning to Execute Subroutines (2020) — 45 citations
• Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks (2021) — 309 citations
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs (2020) — 1,400 citations
• GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019) — 121 citations