Zichao Long

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #20,249 of 35,345 by trust here, #14,718 by citations
• Statistical and Nonlinear Physics — 23 citations into it; #3,485 of 10,127 by trust here, #2,370 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Model Architectures — 12 citations into it
• AI for Physical Sciences — 11 citations into it; #132 of 247 by trust here, #155 by citations
• Numerical Methods & Scientific Computing — 11 citations into it; #189 of 266 by trust here, #109 by citations
• Signal Processing — 8 citations into it
• Computational Mechanics — 6 citations into it; #8,214 of 10,001 by trust here, #8,566 by citations

Whom Zichao Long trusts overall

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

1. Maizar Raissi
2. Kaiming He
3. Stéphane Mallat
4. Hayden Schaeffer
5. W. E
6. Karthik Duraisamy
7. Eldad Haber
8. X. Zhang
9. Georg Martius
10. Yiping Lu

Who trusts Zichao Long overall

1. Vincent Le Guen
2. Tianlong Chen
3. Vishal Monga
4. Nils Thürey
5. Nicolas Thomé
6. Philipp Holl
7. Bing Xiong
8. Mengyu Chu
9. Sungyong Seo
10. Michael Lutter

Papers from Zichao Long's perspective

Ordered by how strongly the researchers Zichao Long 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• PDE-Net: Learning PDEs from Data (2017) — 886 citations
• PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network (2018) — 645 citations