Víctor M. Sánchez-Cartagena

Natural Language Processing · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 84 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 57 citations into it
• Artificial Intelligence — 51 citations into it; #13,473 of 35,345 by trust here, #19,873 by citations
• Evaluation & Benchmarks — 20 citations into it; #2,512 of 3,147 by trust here, #2,627 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #17,974 of 21,575 by trust here, #15,529 by citations
• Model Architectures — 10 citations into it
• Machine Translation — 5 citations into it; #184 of 292 by trust here, #168 by citations
• Optimization & Training — 5 citations into it

Whom Víctor M. Sánchez-Cartagena trusts overall

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

1. Philipp Koehn
2. Franz Joseph Och
3. Rico Sennrich
4. Matthew G. Snover
5. Yoshua Bengio
6. Kyunghyun Cho
7. Barry Haddow
8. Michel Galley
9. Dzmitry Bahdanau
10. Chris Callison-Burch

Who trusts Víctor M. Sánchez-Cartagena overall

1. Maja Popovic
2. Antonio Toral
3. Duygu Ataman
4. Raphaël Rubino
5. Matteo Negri
6. Philipp Koehn
7. Felix Stahlberg
8. Marco Turchi

Papers from Víctor M. Sánchez-Cartagena's perspective

Ordered by how strongly the researchers Víctor M. Sánchez-Cartagena trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions (2017) — 161 citations
• Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences (2016) — 26 citations
• The UA-Prompsit hybrid machine translation system for the 2014 Workshop on Statistical Machine Translation (2014) — 5 citations
• Translation Task : Two-step Data Selection and RBMT-Style Synthetic Rules (2014)