Yu Yang

Artificial Intelligence · with Wu · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 542 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #16,140 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 99 citations into it; #10,313 of 21,575 by trust here, #10,500 by citations
• Safety, Robustness & Interpretability — 39 citations into it
• Model Architectures — 29 citations into it
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Generative Models — 9 citations into it

Whom Yu Yang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Quanshi Zhang
5. Karen Simonyan
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Yu Yang overall

1. Quanshi Zhang
2. Giulia Vilone
3. Stephan Alaniz
4. Song-Chun Zhu
5. Meike Nauta
6. Zhi Chen
7. D. V. Carvalho
8. Xu Cheng
9. Aaron M. Roth
10. Alvin Wan

Papers from Yu Yang's perspective

Ordered by how strongly the researchers Yu Yang 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Interpreting CNNs via Decision Trees (2018) — 353 citations
• Unsupervised Learning of Neural Networks to Explain Neural Networks (2018) — 29 citations
• Network Transplanting (2018) — 5 citations
• A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks (2019) — 16 citations