Jianfeng Lu

Artificial Intelligence · with Khoo · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Scaling & Learning Theory — 10 citations into it
• Statistical and Nonlinear Physics — 9 citations into it; #2,955 of 10,127 by trust here, #4,281 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Model Architectures — 8 citations into it
• Statistical Learning Theory — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Jianfeng Lu trusts overall

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

1. Dmitry Yarotsky
2. Andrew R. Barron
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Martin H. Anthony
6. Diederik P. Kingma
7. Peter L. Bartlett
8. Kaiming He
9. Timothy Dozat
10. George Stefanou

Who trusts Jianfeng Lu overall

1. Ronald A. DeVore
2. Rohit Tripathy
3. Zijie Li
4. Liu Yang
5. Johannes Brandstetter
6. Martin Hutzenthaler
7. Yuwei Fan
8. Boris Hanin
9. Dongkun Zhang

Papers from Jianfeng Lu's perspective

Ordered by how strongly the researchers Jianfeng 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Solving parametric PDE problems with artificial neural networks (2017) — 409 citations
• Neural collapse under cross-entropy loss (2022)
• Deep Network Approximation for Smooth Functions (2020) — 314 citations