Kuang-Huei Lee

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 9 papers; trusts 530 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 275 citations into it; #14,534 of 35,345 by trust here, #2,594 by citations
• Computer Vision and Pattern Recognition — 193 citations into it; #6,885 of 21,575 by trust here, #1,918 by citations
• Reinforcement Learning — 166 citations into it
• Natural Language Processing — 66 citations into it
• Control and Systems Engineering — 57 citations into it
• Multimodal Learning — 42 citations into it; #737 of 1,211 by trust here, #494 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Model Architectures — 16 citations into it

Whom Kuang-Huei Lee trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Richard S. Sutton
8. Ilya Sutskever
9. Jimmy Ba
10. Yann LeCun

Who trusts Kuang-Huei Lee overall

1. Junnan Li
2. Yen-Chun Chen
3. Zhe Gan
4. Görkem Algan
5. Hwanjun Song
6. Linjie Li
7. Jiasen Lu
8. Lu Jiang
9. Amanpreet Singh
10. Jiangfan Han

Papers from Kuang-Huei Lee's perspective

Ordered by how strongly the researchers Kuang-Huei Lee 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — 3,376 citations
• RT-1: Robotics Transformer for Real-World Control at Scale (2022) — 2,545 citations
• Stacked Cross Attention for Image-Text Matching (2018) — 1,394 citations
• CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise (2017) — 515 citations
• Language to Rewards for Robotic Skill Synthesis (2023) — 422 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale (2022) — 12 citations
• Deep Hierarchical Planning from Pixels (2022) — 144 citations
• Predictive Information Accelerates Learning in RL (2020) — 83 citations