Ruiyang Zhang

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it
• Civil and Structural Engineering — 12 citations into it; #1,786 of 6,160 by trust here, #3,042 by citations
• Statistical and Nonlinear Physics — 11 citations into it; #6,123 of 10,127 by trust here, #7,024 by citations
• Statistics and Probability — 11 citations into it
• Model Architectures — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Control and Systems Engineering — 7 citations into it
• Mechanical Engineering — 7 citations into it; #4,120 of 8,300 by trust here, #6,630 by citations

Whom Ruiyang Zhang trusts overall

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

1. Zhong-Zhi Bai
2. Jack P. C. Kleijnen
3. Diederik P. Kingma
4. Danilo P. Mandic
5. V. Ediger
6. Cheng Chen
7. Dong C. Liu
8. A. I. Khuri
9. Ka-Veng Yuen
10. B. Chiou

Who trusts Ruiyang Zhang overall

1. Han Gao
2. Matthew J. Zahr
3. Yang Liu
4. Chengping Rao
5. Jian-Xun Wang
6. Salvatore Cuomo
7. Arman Malekloo
8. Vincenzo Schiano Di Cola
9. Luning Sun

Papers from Ruiyang Zhang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020) — 465 citations
• Deep long short-term memory networks for nonlinear structural seismic response prediction (2019) — 501 citations
• Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019) — 460 citations