Itsumi Saito

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 157 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it
• Natural Language Processing — 116 citations into it
• Computer Vision and Pattern Recognition — 54 citations into it
• Information Retrieval & Search — 29 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 16 citations into it
• Information Systems — 14 citations into it
• Large Language Models — 13 citations into it

Whom Itsumi Saito trusts overall

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

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

Who trusts Itsumi Saito overall

1. Junyi Li
2. Junxian He
3. Kyosuke Nishida
4. Kosuke Nishida
5. Wojciech Kryscinski
6. Jianmo Ni
7. Tianyi Tang

Papers from Itsumi Saito's perspective

Ordered by how strongly the researchers Itsumi Saito 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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
• Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models (2020) — 32 citations