Kevin Lu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 178 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #16,910 of 35,345 by trust here, #7,062 by citations
• Reinforcement Learning — 102 citations into it; #1,780 of 2,242 by trust here, #850 by citations
• Computer Vision and Pattern Recognition — 51 citations into it
• Management Science and Operations Research — 28 citations into it
• Model Architectures — 25 citations into it
• Control and Systems Engineering — 18 citations into it
• Large Language Models — 16 citations into it; #455 of 799 by trust here, #634 by citations
• Electrical and Electronic Engineering — 13 citations into it; #8,482 of 16,539 by trust here, #8,802 by citations

Whom Kevin Lu trusts overall

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

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

Who trusts Kevin Lu overall

1. Shuhao Cao
2. Maciej Wolczyk
3. Michael Gira
4. Gabriele Prato
5. Sergey Levine
6. Ruisu Zhang
7. Machel Reid
8. Richard Ngo
9. Gabriel Recchia
10. Tuan Dinh

Papers from Kevin Lu's perspective

Ordered by how strongly the researchers Kevin 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Decision Transformer: Reinforcement Learning via Sequence Modeling (2021) — 2,391 citations
• Pretrained Transformers as Universal Computation Engines (2021) — 238 citations
• Reset-Free Lifelong Learning with Skill-Space Planning (2020) — 44 citations
• Adaptive Online Planning for Continual Lifelong Learning (2019) — 15 citations