Yuchen Wu

Artificial Intelligence · with Ding · 2020–2023

A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it
• Reinforcement Learning — 31 citations into it
• Generative Models — 24 citations into it
• Control and Systems Engineering — 12 citations into it
• Statistical and Nonlinear Physics — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Yuchen Wu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Andrew Y. Ng
4. Danilo Jimenez Rezende
5. Yoshua Bengio
6. Jonathan Ho
7. Richard S. Sutton
8. Alex Krizhevsky
9. Sergey Levine
10. Ian J. Goodfellow

Papers from Yuchen Wu's perspective

Ordered by how strongly the researchers Yuchen Wu 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation (2023) — 1,142 citations
• Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models (2020) — 26 citations