Bin Dong

Artificial Intelligence · with Lu · 2007–2019

A researcher in the Science corpus: 9 papers; trusts 388 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #10,702 of 35,345 by trust here, #7,797 by citations
• Computer Vision and Pattern Recognition — 107 citations into it; #11,005 of 21,575 by trust here, #10,677 by citations
• Model Architectures — 51 citations into it; #1,597 of 2,278 by trust here, #1,490 by citations
• Statistical and Nonlinear Physics — 31 citations into it; #2,323 of 10,127 by trust here, #1,486 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,379 of 2,251 by trust here, #1,589 by citations
• Computational Mechanics — 28 citations into it; #5,225 of 10,001 by trust here, #7,884 by citations
• Optimization & Training — 25 citations into it
• Signal Processing — 22 citations into it

Whom Bin Dong trusts overall

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

1. Ingrid Daubechies
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ilya Sutskever
9. Stanley J. Osher
10. Amos Ron

Who trusts Bin Dong overall

1. Jian-Feng Cai
2. Vincent Le Guen
3. Zichao Long
4. Eric Wong
5. Zuowei Shen
6. Raymond H. Chan
7. Yiping Lu
8. Nicolas Thomé
9. Bing Xiong
10. Jong Chul Ye

Papers from Bin Dong's perspective

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

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
• Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds (2018) — 24 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
• Image restoration: Total variation, wavelet frames, and beyond (2012) — 349 citations
• PSEUDO-SPLINES, WAVELETS AND FRAMELETS (2007) — 118 citations