Kun Wu

Artificial Intelligence · with Tang · 2020–2024

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Reinforcement Learning — 20 citations into it
• Model Architectures — 8 citations into it

Whom Kun Wu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Jimmy Ba
6. Ilya Sutskever
7. Kaiming He
8. John Schulman
9. Yann LeCun
10. Sergey Levine

Papers from Kun Wu's perspective

Ordered by how strongly the researchers Kun Wu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• TinyVLA: Toward Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation (2024) — 387 citations
• Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control (2020) — 56 citations
• Hierarchical Graph Attention Network for Few-shot Visual-Semantic Learning (2021) — 13 citations