Atsushi Fujita

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 240 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #13,050 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 81 citations into it; #3,169 of 5,688 by trust here, #4,449 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #11,541 of 21,575 by trust here, #15,529 by citations
• Model Architectures — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Efficiency & Compression — 11 citations into it
• Machine Translation — 5 citations into it

Whom Atsushi Fujita trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Yoshua Bengio
4. Rico Sennrich
5. Kyunghyun Cho
6. Oriol Vinyals
7. Dzmitry Bahdanau
8. Diederik P. Kingma
9. Andrew Y. Ng
10. Alex Krizhevsky

Who trusts Atsushi Fujita overall

1. Raj Dabre
2. Yuta Nishimura
3. Amane Sugiyama
4. Surangika Ranathunga
5. Junyi Li
6. Shun Zhang
7. Katsuhito Sudoh
8. Tatiana Likhomanenko
9. Miguel Graça

Papers from Atsushi Fujita's perspective

Ordered by how strongly the researchers Atsushi Fujita 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Softmax Tempering for Training Neural Machine Translation Models (2020) — 13 citations
• Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation (2018) — 77 citations
• Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation (2019) — 63 citations
• Balancing Cost and Benefit with Tied-Multi Transformers (2020) — 7 citations
• NICT’s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task (2019) — 16 citations