Nicholas Lourie

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 268 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #2,864 of 35,345 by trust here, #3,052 by citations
• Natural Language Processing — 204 citations into it; #810 of 5,688 by trust here, #752 by citations
• Evaluation & Benchmarks — 133 citations into it; #2,013 of 3,147 by trust here, #1,467 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #4,717 of 21,575 by trust here, #2,902 by citations
• Large Language Models — 35 citations into it
• Safety, Robustness & Interpretability — 22 citations into it
• Optimization & Training — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Nicholas Lourie trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Ronald J. Williams
7. Yann LeCun
8. Kaiming He
9. Richard Socher
10. Yarin Gal

Who trusts Nicholas Lourie overall

1. Yi Tay
2. Swaroop Mishra
3. Jiacheng Liu
4. Ronan Le Bras
5. Yejin Choi
6. Adyasha Maharana
7. Alon Talmor
8. Chadi Helwe
9. Qinyuan Ye
10. Maarten Sap

Papers from Nicholas Lourie's perspective

Ordered by how strongly the researchers Nicholas Lourie 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (2019) — 2,559 citations
• UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark (2021) — 151 citations
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics (2020) — 601 citations
• Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes (2020) — 156 citations
• G ENIE A Leaderboard for Human-in-the-Loop Evaluation of Text Generation (2021)
• Findings of the 2021 Conference on Machine Translation (WMT21) (2021) — 244 citations
• Learning from Task Descriptions (2020) — 98 citations