José A. R. Fonollosa

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 15 papers; trusts 314 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #3,457 of 35,345 by trust here, #7,241 by citations
• Natural Language Processing — 204 citations into it; #962 of 5,688 by trust here, #1,910 by citations
• Computer Vision and Pattern Recognition — 101 citations into it; #4,150 of 21,575 by trust here, #6,426 by citations
• Model Architectures — 29 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Machine Translation — 15 citations into it
• Safety, Robustness & Interpretability — 8 citations into it; #1,819 of 2,251 by trust here, #2,110 by citations

Whom José A. R. Fonollosa trusts overall

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

1. Philipp Koehn
2. Yoshua Bengio
3. Rico Sennrich
4. Eric R. Ziegel
5. Diederik P. Kingma
6. Kyunghyun Cho
7. Geoffrey E. Hinton
8. Dzmitry Bahdanau
9. Ilya Sutskever
10. Holger Schwenk

Who trusts José A. R. Fonollosa overall

1. Diviyan Kalainathan
2. Jovana Mitrovi'c
3. Yonatan Belinkov
4. Marta Ruiz Costa-jussà
5. Olivier Goudet
6. Felix Stahlberg
7. Nadir Durrani
8. Fahim Dalvi
9. Hassan Sajjad
10. Ronan Collobert

Papers from José A. R. Fonollosa's perspective

Ordered by how strongly the researchers José A. R. Fonollosa 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Character-based Neural Machine Translation (2016) — 345 citations
• 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
• Conditional distribution variability measures for causality detection (2016) — 51 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
• Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information (2020) — 33 citations
• Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders (2020) — 11 citations
• Towards Interlingua Neural Machine Translation (2019) — 5 citations
• BERT Masked Language Modeling for Co-reference Resolution (2019) — 11 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
• Dealing with Input Noise in Statistical Machine Translation (2012) — 17 citations
• Continuous space language models for the IWSLT 2006 task (2006) — 28 citations