Millicent Li

Artificial Intelligence · 2022

A researcher in the Science corpus: 1 paper; trusts 95 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it
• Natural Language Processing — 72 citations into it
• Large Language Models — 47 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it
• Safety, Robustness & Interpretability — 18 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 12 citations into it

Whom Millicent Li trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Graves
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Papers from Millicent Li's perspective

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

Their papers

• A Review on Language Models as Knowledge Bases (2022) — 217 citations