Sarah Wiegreffe
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 8 papers; trusts 962 researchers and is trusted by 108 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 424 citations into it; #2,413 of 35,345 by trust here, #3,289 by citations
- Natural Language Processing — 308 citations into it; #1,042 of 5,688 by trust here, #1,274 by citations
- Computer Vision and Pattern Recognition — 118 citations into it
- Safety, Robustness & Interpretability — 107 citations into it; #318 of 2,251 by trust here, #593 by citations
- Evaluation & Benchmarks — 99 citations into it; #1,057 of 3,147 by trust here, #2,442 by citations
- Prompting & Reasoning — 34 citations into it
- Large Language Models — 30 citations into it
- Information Systems — 22 citations into it
Whom Sarah Wiegreffe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Richard Socher
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Alex Graves
- Jimmy Ba
Who trusts Sarah Wiegreffe overall
- Ana Marasovic
- J'er'emy Scheurer
- Xi Ye
- Alon Jacovi
- Bo Li
- Xiaochuang Han
- Noah A. Smith
- Swarnadeep Saha
- Faeze Brahman
- Dheeraj Rajagopal
Papers from Sarah Wiegreffe's perspective
Ordered by how strongly the researchers Sarah Wiegreffe trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Self-Refine: Iterative Refinement with Self-Feedback (2023) — 4,061 citations
- Attention is not not Explanation (2019) — 1,207 citations
- Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021) — 176 citations
- Measuring Association Between Labels and Free-Text Rationales (2020) — 219 citations
- Learning to Faithfully Rationalize by Construction (2020) — 177 citations
- Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing (2021) — 168 citations
- Explainable Prediction of Medical Codes from Clinical Text (2018) — 692 citations
- Teach Me to Explain: A Review of Datasets for Explainable NLP (2021) — 70 citations