Kellie Webster

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 12 papers; trusts 231 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 365 citations into it; #10,397 of 35,345 by trust here, #3,440 by citations
• Natural Language Processing — 251 citations into it; #1,938 of 5,688 by trust here, #1,360 by citations
• Safety, Robustness & Interpretability — 149 citations into it; #1,092 of 2,251 by trust here, #472 by citations
• Computer Vision and Pattern Recognition — 101 citations into it
• Evaluation & Benchmarks — 76 citations into it; #2,758 of 3,147 by trust here, #3,004 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Large Language Models — 36 citations into it
• Safety Research — 30 citations into it; #4,746 of 5,829 by trust here, #2,212 by citations

Whom Kellie Webster trusts overall

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

1. Jacob Devlin
2. Jörg Tiedemann
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Kenton C. T. Lee
7. Ilya Sutskever
8. Christopher D. Manning
9. Ashish Vaswani
10. Tomas Mikolov

Who trusts Kellie Webster overall

1. Eric Michael Smith
2. Adina Williams
3. Su Lin Blodgett
4. Yuval Varkel
5. Adithya Renduchintala
6. Oskar van der Wal
7. Sunipa Dev
8. Mandar Joshi
9. Timothee Mickus
10. Shaily Bhatt

Papers from Kellie Webster's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021) — 1,261 citations
• Social Biases in NLP Models as Barriers for Persons with Disabilities (2020) — 378 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy (2021) — 11 citations
• They, Them, Theirs: Rewriting with Gender-Neutral English (2021) — 66 citations
• Resolving Gendered Ambiguous Pronouns with BERT (2019) — 50 citations
• Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias (2020) — 27 citations
• Automatically Identifying Gender Issues in Machine Translation using Perturbations (2020) — 46 citations
• A Challenge Set and Methods for Noun-Verb Ambiguity (2018) — 18 citations
• Measuring and Reducing Gendered Correlations in Pre-trained Models (2020) — 313 citations
• Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020) — 5 citations
• Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019 (2019) — 23 citations