Danielle Saunders

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 154 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it; #8,100 of 35,345 by trust here, #13,544 by citations
• Natural Language Processing — 120 citations into it; #1,694 of 5,688 by trust here, #2,888 by citations
• Safety, Robustness & Interpretability — 53 citations into it; #750 of 2,251 by trust here, #1,780 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Language and Linguistics — 12 citations into it
• Optimization & Training — 8 citations into it
• Machine Translation — 7 citations into it

Whom Danielle Saunders trusts overall

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

1. Matt Post
2. Rico Sennrich
3. Ashish Vaswani
4. Barry Haddow
5. Jieyu Zhao
6. Philipp Koehn
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Danielle Saunders overall

1. James Thorne
2. Emily Sheng
3. Karolina Stańczak
4. Bill Byrne
5. Andreas Vlachos
6. Beatrice Savoldi
7. Toms Bergmanis
8. Rosie Sallis
9. Kai-Wei Chang
10. Isabelle Augenstein

Papers from Danielle Saunders's perspective

Ordered by how strongly the researchers Danielle Saunders 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020) — 158 citations
• Neural Machine Translation Doesn’t Translate Gender Coreference Right Unless You Make It (2020) — 69 citations
• First the Worst: Finding Better Gender Translations During Beam Search (2021) — 38 citations
• The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing (2021) — 46 citations
• Domain Adaptive Inference for Neural Machine Translation (2019) — 32 citations