Rachel Bawden

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 6 papers; trusts 61 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 174 citations into it; #13,137 of 35,345 by trust here, #2,908 by citations
• Natural Language Processing — 123 citations into it; #2,471 of 5,688 by trust here, #2,132 by citations
• Large Language Models — 62 citations into it
• Evaluation & Benchmarks — 51 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it; #8,652 of 21,575 by trust here, #11,721 by citations
• Model Architectures — 14 citations into it
• Efficiency & Compression — 13 citations into it
• Prompting & Reasoning — 11 citations into it

Whom Rachel Bawden trusts overall

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

1. Rico Sennrich
2. Dzmitry Bahdanau
3. Yoshua Bengio
4. Kyunghyun Cho
5. Matt Post
6. Geoffrey E. Hinton
7. Pierre Lison
8. Barry Haddow
9. Jörg Tiedemann
10. Ilya Sutskever

Who trusts Rachel Bawden overall

1. Yonatan Belinkov
2. Felix Stahlberg
3. Liane Guillou
4. Kellie Webster
5. Dario Stojanovski
6. Amane Sugiyama
7. James R. Glass
8. Christian Hardmeier
9. Lei Yu
10. Adam Poliak

Papers from Rachel Bawden's perspective

Ordered by how strongly the researchers Rachel Bawden 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Multitask Prompted Training Enables Zero-Shot Task Generalization (2021) — 2,030 citations
• Evaluating Discourse Phenomena in Neural Machine Translation (2017) — 287 citations
• DiaBLa: a corpus of bilingual spontaneous written dialogues for machine translation (2019) — 18 citations
• Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM (2023) — 100 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Multitask Prompted Training Enables Zero-Shot Task Generalization (2022) — 242 citations