Zongrui Pei

Mechanical Engineering · 2012–2022

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Mechanical Engineering — 103 citations into it
• Materials Chemistry — 80 citations into it
• Aerospace Engineering — 62 citations into it
• Computational Theory and Mathematics — 23 citations into it
• Biomedical Engineering — 21 citations into it
• Artificial Intelligence — 18 citations into it
• Atomic and Molecular Physics, and Optics — 10 citations into it
• Computational Chemistry & Drug Discovery — 9 citations into it

Whom Zongrui Pei trusts overall

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

1. A. P. Bart'ok
2. J. Behler
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Benjamin Peherstorfer
7. Yann LeCun
8. Stefan Heinrich
9. M. Giles
10. Gábor Csányi

Who trusts Zongrui Pei overall

1. Xianglin Liu

Papers from Zongrui Pei's perspective

Ordered by how strongly the researchers Zongrui Pei trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The relation between ductility and stacking fault energies in Mg and Mg–Y alloys (2012) — 577 citations
• Ductility improvement of Mg alloys by solid solution: Ab initio modeling, synthesis and mechanical properties (2014) — 304 citations
• Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022) — 323 citations
• Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules (2020) — 191 citations