Kyosuke Nishida

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 Kyosuke Nishida 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. Alex Graves
8. Kaiming He
9. Jimmy Ba
10. Sepp Hochreiter

Who trusts Kyosuke Nishida overall

1. Kosuke Nishida
2. Jianmo Ni
3. Bin Bi
4. Peng Qi
5. Yuyu Zhang
6. Hao Cheng
7. Wasi Uddin Ahmad
8. Fengbin Zhu
9. Yu Wang
10. Chenliang Li

Papers from Kyosuke Nishida's perspective

Ordered by how strongly the researchers Kyosuke Nishida 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. 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

• 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