Craig Alan Stewart

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 61 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #10,854 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 81 citations into it; #2,427 of 5,688 by trust here, #2,634 by citations
• Evaluation & Benchmarks — 72 citations into it; #1,227 of 3,147 by trust here, #1,638 by citations
• Information Systems — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Machine Translation — 7 citations into it

Whom Craig Alan Stewart trusts overall

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

1. Yvette Graham
2. Matthew G. Snover
3. Ondrej Bojar
4. Jeremy Howard
5. Qingsong Ma
6. Thibault Sellam
7. Tomas Mikolov
8. Ilya Sutskever
9. Geoffrey E. Hinton
10. Yoshua Bengio

Who trusts Craig Alan Stewart overall

1. Ricardo Rei
2. Markus Freitag
3. Nitika Mathur
4. Tom Kocmi
5. Sebastian Gehrmann
6. Rachel Bawden
7. Thibault Sellam
8. Christian Federmann
9. Wenda Xu
10. Amy Pu

Papers from Craig Alan Stewart's perspective

Ordered by how strongly the researchers Craig Alan Stewart 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet classification with deep convolutional neural networks
7. Bleu: a Method for Automatic Evaluation of Machine Translation
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• COMET: A Neural Framework for MT Evaluation (2020) — 1,636 citations
• Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust (2022) — 351 citations
• Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain (2021) — 206 citations
• Unbabel’s Participation in the WMT20 Metrics Shared Task (2020) — 91 citations