Craig Thomson

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 46 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #15,244 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 87 citations into it; #2,773 of 5,688 by trust here, #4,112 by citations
• Evaluation & Benchmarks — 59 citations into it; #1,241 of 3,147 by trust here, #2,369 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #9,726 of 21,575 by trust here, #17,258 by citations
• Information Systems — 12 citations into it; #5,551 of 8,771 by trust here, #8,676 by citations
• Natural Language Generation — 11 citations into it

Whom Craig Thomson trusts overall

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

1. Ondrej Dusek
2. Sam Wiseman
3. Ehud Reiter
4. Jekaterina Novikova
5. Ratish Puduppully
6. Stuart M. Shieber
7. Ani Nenkova
8. Verena Rieser
9. Chris van der Lee
10. James Thorne

Who trusts Craig Thomson overall

1. Sebastian Gehrmann
2. Ehud Reiter
3. Elizabeth Clark
4. Thibault Sellam

Papers from Craig Thomson's perspective

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

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

Their papers

• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• Underreporting of errors in NLG output, and what to do about it (2021) — 42 citations
• A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems (2020) — 61 citations
• Shared Task on Evaluating Accuracy (2020) — 9 citations
• Generation Challenges: Results of the Accuracy Evaluation Shared Task (2021) — 21 citations