Ke Lu

Artificial Intelligence · with Li · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 238 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Representation & Self-Supervised Learning — 43 citations into it
• Generative Models — 10 citations into it

Whom Ke Lu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Ross B. Girshick
8. Ilya Sutskever
9. Mingsheng Long
10. Karen Simonyan

Papers from Ke Lu's perspective

Ordered by how strongly the researchers Ke 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Leveraging the Invariant Side of Generative Zero-Shot Learning (2019) — 338 citations
• Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation (2021) — 208 citations
• Maximum Density Divergence for Domain Adaptation (2020) — 340 citations