Shiyu Liang

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 166 researchers and is trusted by 94 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #3,028 of 35,345 by trust here, #4,747 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #3,431 of 21,575 by trust here, #4,317 by citations
• Model Architectures — 14 citations into it
• Safety, Robustness & Interpretability — 14 citations into it; #208 of 2,251 by trust here, #456 by citations
• Evaluation & Benchmarks — 13 citations into it
• Natural Language Processing — 13 citations into it
• Optimization & Training — 8 citations into it
• Information Systems — 7 citations into it

Whom Shiyu Liang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ian J. Goodfellow
6. Ilya Sutskever
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Christian Szegedy

Who trusts Shiyu Liang overall

1. Kimin Lee
2. Yixuan Li
3. Rui Huang
4. Hyun-Jae Choi
5. Dan Hendrycks
6. Yiyou Sun
7. Jingkang Yang
8. Gyuhak Kim
9. Danijar Hafner
10. Zhizhong Li

Papers from Shiyu Liang's perspective

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

Their papers

• Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks (2017) — 2,544 citations
• Why Deep Neural Networks for Function Approximation? (2016) — 412 citations
• Principled Detection of Out-of-Distribution Examples in Neural Networks (2017) — 128 citations