Yoshimasa Tsuruoka

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 9 papers; trusts 385 researchers and is trusted by 175 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #1,832 of 35,345 by trust here, #3,835 by citations
• Natural Language Processing — 127 citations into it; #1,787 of 5,688 by trust here, #1,735 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #3,448 of 21,575 by trust here, #3,489 by citations
• Model Architectures — 54 citations into it
• Evaluation & Benchmarks — 19 citations into it; #2,587 of 3,147 by trust here, #2,743 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Optimization & Training — 13 citations into it
• Molecular Biology — 11 citations into it; #16,239 of 17,773 by trust here, #14,833 by citations

Whom Yoshimasa Tsuruoka trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Michael Collins
5. Christopher D. Manning
6. Kyunghyun Cho
7. Richard Socher
8. Tomas Mikolov
9. Diederik P. Kingma
10. Alex Graves

Who trusts Yoshimasa Tsuruoka overall

1. Zhiyong Lu
2. Kazuma Hashimoto
3. Mark Neumann
4. Christopher D. Manning
5. André F. T. Martins
6. Yuan Luo
7. Marek Rei
8. Caiming Xiong
9. Yonatan Belinkov
10. Libin Shen

Papers from Yoshimasa Tsuruoka's perspective

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

Their papers

• A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks (2016) — 589 citations
• Learning to Parse and Translate Improves Neural Machine Translation (2017) — 153 citations
• Tree-to-Sequence Attentional Neural Machine Translation (2016) — 279 citations
• Modeling Latent-Dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference (2008) — 67 citations
• Multilingual Extractive Reading Comprehension by Runtime Machine Translation (2018) — 61 citations
• Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data (2005) — 286 citations
• Neural Machine Translation with Source-Side Latent Graph Parsing (2017) — 48 citations
• Developing a Robust Part-of-Speech Tagger for Biomedical Text (2005) — 536 citations
• Accelerating the annotation of sparse named entities by dynamic sentence selection (2008) — 39 citations