Ricardo Rei
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 6 papers; trusts 80 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 113 citations into it; #10,777 of 35,345 by trust here, #11,192 by citations
- Natural Language Processing — 104 citations into it; #2,468 of 5,688 by trust here, #2,634 by citations
- Evaluation & Benchmarks — 97 citations into it; #1,239 of 3,147 by trust here, #1,638 by citations
- Computer Vision and Pattern Recognition — 22 citations into it
- Information Systems — 13 citations into it
- Representation & Self-Supervised Learning — 12 citations into it
- Machine Translation — 10 citations into it
Whom Ricardo Rei trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yvette Graham
- Ondrej Bojar
- Matthew G. Snover
- Qingsong Ma
- Thibault Sellam
- Chi-kiu (羅致翹) Lo
- Maja Popovic
- Nitika Mathur
- Markus Freitag
- Jeremy Howard
Who trusts Ricardo Rei overall
- Markus Freitag
- Tom Kocmi
- Nitika Mathur
- Sebastian Gehrmann
- Christian Federmann
- Thibault Sellam
- Rachel Bawden
- Wenda Xu
- Biao Zhang
- Weizhe Yuan
Papers from Ricardo Rei's perspective
Ordered by how strongly the researchers Ricardo Rei trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Bleu: a Method for Automatic Evaluation of Machine Translation
- 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
- xcomet: Transparent Machine Translation Evaluation through Fine-grained Error Detection (2023) — 330 citations
- COMET-22: Unbabel-IST 2022 Submission for the Metrics Shared Task (2022) — 503 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