Kevin Meng

Artificial Intelligence · 2022

A researcher in the Science corpus: 3 papers; trusts 78 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #21,730 of 35,345 by trust here, #8,893 by citations
• Natural Language Processing — 51 citations into it
• Large Language Models — 44 citations into it; #428 of 799 by trust here, #455 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Model Architectures — 19 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Management Science and Operations Research — 11 citations into it
• Causal Inference & Discovery — 10 citations into it

Whom Kevin Meng trusts overall

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

1. Judea Pearl
2. Diederik P. Kingma
3. Teuvo Kohonen
4. Geoffrey E. Hinton
5. James A. Anderson
6. Yoshua Bengio
7. Ilya Sutskever
8. Alex Krizhevsky
9. Jimmy Ba
10. Yonatan Belinkov

Who trusts Kevin Meng overall

1. Shilin Lu
2. Zexuan Zhong
3. Mor Geva
4. Leonard Bereska
5. Wes Gurnee
6. Arthur Conmy
7. Charles Burton Snell
8. Ce Zheng
9. Jian Xie
10. Thomas Hartvigsen

Papers from Kevin Meng's perspective

Ordered by how strongly the researchers Kevin Meng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Locating and Editing Factual Associations in GPT (2022) — 2,886 citations
• Mass-Editing Memory in a Transformer (2022) — 1,108 citations
• Locating and Editing Factual Knowledge in GPT (2022) — 83 citations