Hideaki Takeda

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 4 papers; trusts 84 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #8,068 of 35,345 by trust here, #8,893 by citations
• Natural Language Processing — 69 citations into it; #1,713 of 5,688 by trust here, #2,473 by citations
• Representation & Self-Supervised Learning — 28 citations into it; #2,110 of 3,145 by trust here, #2,691 by citations
• Information Systems — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Information Extraction — 7 citations into it
• Communication — 5 citations into it
• Information Retrieval & Search — 5 citations into it

Whom Hideaki Takeda trusts overall

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

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

Who trusts Hideaki Takeda overall

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

Papers from Hideaki Takeda's perspective

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

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

• 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
• Learning Distributed Representations of Texts and Entities from Knowledge Base (2017) — 103 citations
• POLYPHONET: an advanced social network extraction system from the web (2006) — 345 citations