Yiping Lu

Artificial Intelligence · with Dong · 2017–2019

A researcher in the Science corpus: 6 papers; trusts 287 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #10,703 of 35,345 by trust here, #7,797 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #14,944 of 21,575 by trust here, #12,419 by citations
• Model Architectures — 44 citations into it; #1,596 of 2,278 by trust here, #1,490 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,382 of 2,251 by trust here, #1,589 by citations
• Statistical and Nonlinear Physics — 28 citations into it; #2,324 of 10,127 by trust here, #1,486 by citations
• Optimization & Training — 23 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Numerical Methods & Scientific Computing — 13 citations into it; #191 of 266 by trust here, #109 by citations

Whom Yiping Lu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. X. Zhang
6. Yann LeCun
7. Diederik P. Kingma
8. Karen Simonyan
9. Ilya Sutskever
10. Shaoqing Ren

Who trusts Yiping Lu overall

1. Vincent Le Guen
2. Zichao Long
3. Nicolas Thomé
4. Eric Wong
5. A. Queiruga
6. Jens Behrmann
7. Xuechen Li
8. David Kristjanson Duvenaud
9. Bin Dong
10. Tianlong Chen

Papers from Yiping Lu's perspective

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

Their papers

• You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle (2019) — 394 citations
• Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View (2019) — 234 citations
• 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
• You Only Propagate Once: Painless Adversarial Training Using Maximal Principle (2019) — 65 citations
• Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations (2017) — 565 citations