Margot Mieskes

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it
• Natural Language Processing — 98 citations into it; #2,542 of 5,688 by trust here, #5,092 by citations
• Evaluation & Benchmarks — 51 citations into it; #2,205 of 3,147 by trust here, #3,134 by citations
• Information Systems — 18 citations into it
• Philosophy — 12 citations into it
• Molecular Biology — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Optimization & Training — 7 citations into it

Whom Margot Mieskes trusts overall

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

1. Chin-Yew Lin
2. George Giannakopoulos
3. Yvette Graham
4. Jerome H. Friedman
5. Ani Nenkova
6. Philip Resnik
7. Günes Erkan
8. Piotr Bojanowski
9. Peter A. Rankel
10. Alexis Conneau

Who trusts Margot Mieskes overall

1. Yang Gao
2. Sebastian Gehrmann
3. Brielen Madureira
4. Yen-Chun Chen
5. Shay B. Cohen
6. Christian M. Meyer
7. Mohit Bansal

Papers from Margot Mieskes's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
3. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Bleu: a Method for Automatic Evaluation of Machine Translation
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Language Models are Unsupervised Multitask Learners
10. Gradient-based learning applied to document recognition

Their papers

• BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model (2022) — 15 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Language Agnostic Automatic Summarization Evaluation (2020) — 4 citations
• A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization (2015) — 23 citations