Haifeng Jin

Artificial Intelligence · 2018

A researcher in the Science corpus: 4 papers; trusts 217 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #15,110 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #9,724 of 21,575 by trust here, #12,172 by citations
• Neural Architecture Search & AutoML — 31 citations into it; #71 of 283 by trust here, #115 by citations
• Model Architectures — 23 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Optimization & Training — 18 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Haifeng Jin trusts overall

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

1. Alex Krizhevsky
2. Frank Hutter
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jasper Snoek
7. Ian J. Goodfellow
8. Kaiming He
9. Hugo Larochelle
10. Tianqi Chen

Who trusts Haifeng Jin overall

1. Colin White
2. Marius Thomas Lindauer
3. Liam Li
4. Jiancheng Yang
5. Qingquan Song
6. Willie Neiswanger
7. Yash Savani
8. Sebastian Raschka
9. Xinyu Gong
10. Andrei Paleyes

Papers from Haifeng Jin's perspective

Ordered by how strongly the researchers Haifeng Jin 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Auto-Keras: An Efficient Neural Architecture Search System (2018) — 924 citations
• Auto-Keras: Efficient Neural Architecture Search with Network Morphism (2018) — 63 citations
• Multi-label Adversarial Perturbations (2018) — 43 citations
• Efficient Neural Architecture Search with Network Morphism (2018) — 81 citations