Phoebe Mulcaire

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 44 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 140 citations into it
• Natural Language Processing — 115 citations into it; #4,400 of 5,688 by trust here, #3,014 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Representation & Self-Supervised Learning — 15 citations into it; #2,511 of 3,145 by trust here, #2,624 by citations
• Large Language Models — 10 citations into it

Whom Phoebe Mulcaire trusts overall

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

1. Christopher D. Manning
2. Matthew E. Peters
3. Guillaume Lample
4. Alexis Conneau
5. Geoffrey E. Hinton
6. Jacob Devlin
7. Diederik P. Kingma
8. Yoshua Bengio
9. Timothy Dozat
10. Tomas Mikolov

Who trusts Phoebe Mulcaire overall

1. Shijie Wu
2. Akari Asai
3. Mark Dredze
4. Xinyan Velocity Yu
5. Chia-Hsuan Lee

Papers from Phoebe Mulcaire's perspective

Ordered by how strongly the researchers Phoebe Mulcaire 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. 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

• Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020) — 462 citations
• Evaluating NLP Models via Contrast Sets (2020) — 85 citations
• Polyglot Contextual Representations Improve Crosslingual Transfer (2019) — 73 citations