Akiko Eriguchi

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 251 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #4,662 of 35,345 by trust here, #5,002 by citations
• Natural Language Processing — 99 citations into it; #3,257 of 5,688 by trust here, #1,910 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #4,587 of 21,575 by trust here, #6,031 by citations
• Model Architectures — 29 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Evaluation & Benchmarks — 14 citations into it; #2,590 of 3,147 by trust here, #2,743 by citations
• Optimization & Training — 7 citations into it
• Machine Translation — 6 citations into it

Whom Akiko Eriguchi trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Kyunghyun Cho
7. Tomas Mikolov
8. Dzmitry Bahdanau
9. Richard Socher
10. Sepp Hochreiter

Who trusts Akiko Eriguchi overall

1. Kazuma Hashimoto
2. Guillaume Lample
3. Carlos Escolano
4. Roee Aharoni
5. Yoshimasa Tsuruoka
6. Yonatan Belinkov
7. Maria Nadejde
8. Kyunghyun Cho
9. Antonios Anastasopoulos
10. Marco Damonte

Papers from Akiko Eriguchi's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to Parse and Translate Improves Neural Machine Translation (2017) — 153 citations
• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• Tree-to-Sequence Attentional Neural Machine Translation (2016) — 279 citations
• Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation (2018) — 65 citations
• Multilingual Extractive Reading Comprehension by Runtime Machine Translation (2018) — 61 citations