Ikuya Yamada

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 236 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #6,292 of 35,345 by trust here, #6,664 by citations
• Natural Language Processing — 112 citations into it; #1,463 of 5,688 by trust here, #1,837 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #13,198 of 21,575 by trust here, #11,950 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #2,109 of 3,145 by trust here, #2,691 by citations
• Evaluation & Benchmarks — 27 citations into it; #2,820 of 3,147 by trust here, #2,853 by citations
• Information Retrieval & Search — 22 citations into it; #1,022 of 1,022 by trust here, #844 by citations
• Information Systems — 15 citations into it
• Information Extraction — 12 citations into it; #384 of 407 by trust here, #338 by citations

Whom Ikuya Yamada trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Richard Socher
9. Tomas Mikolov
10. Kyunghyun Cho

Who trusts Ikuya Yamada overall

1. Samuel Broscheit
2. Phong Le
3. Jonathan Raiman
4. Ivan Titov
5. Nicola De Cao
6. Pedro Henrique Martins
7. Yasumasa Onoe
8. Octavian-Eugen Ganea
9. Akari Asai
10. Jeffrey Ling

Papers from Ikuya Yamada's perspective

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

Their papers

• Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering (2018) — 169 citations
• Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation (2016) — 337 citations
• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020) — 780 citations
• Named Entity Disambiguation for Noisy Text (2017) — 101 citations
• Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021) — 99 citations
• Learning Distributed Representations of Texts and Entities from Knowledge Base (2017) — 103 citations
• Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples (2019) — 28 citations