Yi-Wei Chen

Artificial Intelligence · with Hu · 2006–2020

A researcher in the Science corpus: 3 papers; trusts 239 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #7,263 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #5,401 of 21,575 by trust here, #11,083 by citations
• Optimization & Training — 33 citations into it
• Neural Architecture Search & AutoML — 21 citations into it
• Model Architectures — 19 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Statistical Learning Theory — 9 citations into it

Whom Yi-Wei Chen trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Kaiming He
6. Diederik P. Kingma
7. Quoc V. Le
8. Barret Zoph
9. Ilya Sutskever
10. Yuhai Wu

Who trusts Yi-Wei Chen overall

1. Mehmet Fatih Akay
2. Julian McAuley
3. Juanying Xie
4. Arti Patle
5. Dmitry Storcheus
6. Martina Lindorfer
7. Majdi Bsoul
8. Andreas Richtsfeld
9. Gregory Sliwoski
10. Zhancheng Zhang

Papers from Yi-Wei Chen's perspective

Ordered by how strongly the researchers Yi-Wei Chen 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On Robustness of Neural Architecture Search Under Label Noise (2020) — 11 citations
• Techniques for Automated Machine Learning (2019) — 66 citations
• Combining SVMs with Various Feature Selection Strategies (2006) — 1,117 citations