Yi-Qi Hu

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 190 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 184 citations into it; #13,910 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Optimization & Training — 64 citations into it
• Neural Architecture Search & AutoML — 53 citations into it; #244 of 283 by trust here, #145 by citations
• Computational Theory and Mathematics — 42 citations into it
• Model Architectures — 31 citations into it
• Management Science and Operations Research — 28 citations into it; #5,317 of 11,946 by trust here, #8,062 by citations
• Evaluation & Benchmarks — 22 citations into it

Whom Yi-Qi Hu trusts overall

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

1. Yoshua Bengio
2. Frank Hutter
3. James Bergstra
4. Yann LeCun
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Kevin Swersky
8. Yang Yu
9. Diederik P. Kingma
10. L. Eon Bottou

Who trusts Yi-Qi Hu overall

1. Yang Yu
2. Quanming Yao
3. Tong Yu
4. Radwa El Shawi
5. Wei-Wei Tu
6. Qiang Yang
7. Hong Zhu
8. Mengshuo Wang
9. Mohamed Maher

Papers from Yi-Qi Hu's perspective

Ordered by how strongly the researchers Yi-Qi Hu 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Automated Machine Learning: From Principles to Practices (2018) — 264 citations
• Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion (2019) — 37 citations
• Sequential Classification-Based Optimization for Direct Policy Search (2017) — 51 citations
• Derivative-Free Optimization via Classification (2016) — 96 citations
• Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search (2018) — 13 citations
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018) — 216 citations