Xin He

Artificial Intelligence · with Chu · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 561 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 219 citations into it; #17,628 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 174 citations into it
• Neural Architecture Search & AutoML — 114 citations into it; #135 of 283 by trust here, #139 by citations
• Optimization & Training — 45 citations into it
• Model Architectures — 40 citations into it
• Efficiency & Compression — 31 citations into it
• Evaluation & Benchmarks — 31 citations into it
• Computational Theory and Mathematics — 17 citations into it

Whom Xin He trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Karen Simonyan
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Ross B. Girshick
10. Quoc V. Le

Who trusts Xin He overall

1. Marc-André Zöller
2. Yi-Wei Chen
3. Sebastian Raschka
4. Mostafa Dehghani
5. Eduardo Mosqueira-Rey
6. Basil Mustafa
7. Marco F. Huber
8. Yufan Jiang
9. Pengcheng Yin
10. Bingzhi Chen

Papers from Xin He's perspective

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

Their papers

• AutoML: A Survey of the State-of-the-Art (2019) — 1,830 citations
• Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020) — 61 citations