Li Lucy
Natural Language Processing · 2017–2021
A researcher in the Science corpus: 2 papers; trusts 56 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 33 citations into it; #3,022 of 5,688 by trust here, #4,601 by citations
- Artificial Intelligence — 30 citations into it; #10,469 of 35,345 by trust here, #15,236 by citations
- Safety, Robustness & Interpretability — 11 citations into it; #982 of 2,251 by trust here, #1,650 by citations
- Representation & Self-Supervised Learning — 10 citations into it; #1,811 of 3,145 by trust here, #2,624 by citations
- Social Psychology — 6 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
- Developmental and Educational Psychology — 5 citations into it
Whom Li Lucy trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Peter D. Turney
- Tomas Mikolov
- George A. Miller
- Ethan Fast
- Lawrence W. Barsalou
- Jeffrey Pennington
- David Bamman
- T. Landauer
- Jon Gauthier
- Ilya Sutskever
Who trusts Li Lucy overall
- Anastasia Chan
- Jeff Da
- Myra Cheng
- Esin Durmus
- Maxwell Forbes
- Roberto Navigli
- Jieyu Zhang
- Ari Holtzman
- Jungo Kasai
- Yejin Choi
Papers from Li Lucy's perspective
Ordered by how strongly the researchers Li Lucy trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017) — 65 citations
- Gender and Representation Bias in GPT-3 Generated Stories (2021) — 410 citations