Carlos Escolano

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 11 papers; trusts 241 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 222 citations into it; #33,268 of 35,345 by trust here, #17,110 by citations
• Natural Language Processing — 211 citations into it
• Computer Vision and Pattern Recognition — 101 citations into it; #21,458 of 21,575 by trust here, #15,955 by citations
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 25 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Machine Translation — 16 citations into it
• Optimization & Training — 10 citations into it

Whom Carlos Escolano trusts overall

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

1. Philipp Koehn
2. Rico Sennrich
3. Diederik P. Kingma
4. Jörg Tiedemann
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Ilya Sutskever
8. Kyunghyun Cho
9. Alexis Conneau
10. Dzmitry Bahdanau

Who trusts Carlos Escolano overall

1. Biao Zhang
2. Wanying Xie
3. Xavier García
4. Orhan Firat
5. Ankur Bapna
6. Marta Ruiz Costa-jussà
7. Felix Stahlberg
8. Yang Feng

Papers from Carlos Escolano's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT (2019) — 16 citations
• Byte-based Neural Machine Translation (2017) — 32 citations
• Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2020) — 60 citations
• From Bilingual to Multilingual Neural Machine Translation by Incremental Training (2019) — 33 citations
• From bilingual to multilingual neural‐based machine translation by incremental training (2019) — 26 citations
• Toxicity in Multilingual Machine Translation at Scale (2022) — 33 citations
• Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters (2020) — 20 citations
• Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders (2020) — 11 citations
• Towards Interlingua Neural Machine Translation (2019) — 5 citations
• (Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation (2018) — 9 citations
• The TALP–UPC Spanish–English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System (2016) — 13 citations