Kosuke Nishida
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 103 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 97 citations into it
- Natural Language Processing — 91 citations into it
- Computer Vision and Pattern Recognition — 41 citations into it
- Evaluation & Benchmarks — 20 citations into it
- Large Language Models — 12 citations into it
- Model Architectures — 11 citations into it
- Optimization & Training — 8 citations into it
Whom Kosuke Nishida trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Alex Graves
- Kaiming He
- Yann LeCun
- Richard Socher
Who trusts Kosuke Nishida overall
- Yuyu Zhang
- Akari Asai
- Hendrik Schuff
- Ping Nie
- Dirk Groeneveld
- Daniel Khashabi
- Yuwei Fang
- Chen Zhao
- Ming Tu
- Tushar Khot
Papers from Kosuke Nishida's perspective
Ordered by how strongly the researchers Kosuke Nishida trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- 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