Atsushi Otsuka

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 125 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it
• Natural Language Processing — 99 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Information Retrieval & Search — 29 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Information Systems — 14 citations into it
• Model Architectures — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Atsushi Otsuka trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Richard Socher
7. Sepp Hochreiter
8. Christopher D. Manning
9. Jimmy Ba
10. Yann LeCun

Papers from Atsushi Otsuka's perspective

Ordered by how strongly the researchers Atsushi Otsuka 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
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

• Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension (2018) — 53 citations
• Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (2019) — 109 citations
• Multi-style Generative Reading Comprehension (2019) — 71 citations