Joel Escudé Font

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 22 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #8,230 of 35,345 by trust here, #12,974 by citations
• Natural Language Processing — 15 citations into it; #1,711 of 5,688 by trust here, #2,767 by citations
• Safety, Robustness & Interpretability — 9 citations into it; #566 of 2,251 by trust here, #1,133 by citations

Whom Joel Escudé Font trusts overall

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

1. Jieyu Zhao
2. Dzmitry Bahdanau
3. Jeffrey Pennington
4. Yoshua Bengio
5. Tolga Bolukbasi
6. Richard Socher
7. Kyunghyun Cho
8. Kai-Wei Chang
9. Philipp Koehn
10. Tomas Mikolov

Who trusts Joel Escudé Font overall

1. Marta Ruiz Costa-jussà
2. Ninareh Mehrabi
3. Karolina Stańczak
4. Nizar Habash
5. Benjamin Hoover
6. Marcus Tomalin
7. Danielle Saunders
8. Christine Basta
9. Isabelle Augenstein
10. Adrià de Jorge

Papers from Joel Escudé Font's perspective

Ordered by how strongly the researchers Joel Escudé Font 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques (2019) — 182 citations