Fernando Alva-Manchego
Natural Language Processing · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 17 citations into it; #4,608 of 5,688 by trust here, #4,539 by citations
- Artificial Intelligence — 16 citations into it; #18,143 of 35,345 by trust here, #20,836 by citations
- Evaluation & Benchmarks — 10 citations into it; #1,666 of 3,147 by trust here, #2,410 by citations
Whom Fernando Alva-Manchego trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Wei Xu
- Sander Wubben
- Elior Sulem
- Zhemin Zhu
- Courtney Napoles
- Christopher D. Manning
- Antal van den Bosch
- Omri Abend
- Salim Roukos
- Ashish Vaswani
Who trusts Fernando Alva-Manchego overall
- Jordan Clive
- Thomas Scialom
- Kris Cao
- Marek Rei
- Tuhin Chakrabarty
- Angelina McMillan-Major
- Louis Martin
- Smaranda Muresan
Papers from Fernando Alva-Manchego's perspective
Ordered by how strongly the researchers Fernando Alva-Manchego trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020) — 192 citations
- EASSE: Easier Automatic Sentence Simplification Evaluation (2019) — 149 citations