Masaaki Nagata

Artificial Intelligence · 1994–2021

A researcher in the Science corpus: 14 papers; trusts 488 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 245 citations into it; #12,069 of 35,345 by trust here, #9,557 by citations
• Natural Language Processing — 232 citations into it; #1,284 of 5,688 by trust here, #2,045 by citations
• Computer Vision and Pattern Recognition — 84 citations into it
• Evaluation & Benchmarks — 78 citations into it
• Model Architectures — 55 citations into it
• Optimization & Training — 21 citations into it
• Large Language Models — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Masaaki Nagata trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Sepp Hochreiter
6. Oriol Vinyals
7. Alex Krizhevsky
8. Tomas Mikolov
9. Sumit Chopra
10. Yann LeCun

Who trusts Masaaki Nagata overall

1. Taku Kudo
2. Graham Neubig
3. Tao Ge
4. Yosuke Nakata
5. Sho Takase
6. Jun Suzuki
7. Angela Fan
8. Shun Kiyono
9. Furu Wei
10. Shinsuke Mori

Papers from Masaaki Nagata's perspective

Ordered by how strongly the researchers Masaaki Nagata 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. Long Short-Term Memory
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization (2016) — 87 citations
• Neural Headline Generation on Abstract Meaning Representation (2016) — 172 citations
• Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction (2019) — 109 citations
• Findings of the 2020 Conference on Machine Translation (WMT20) (2020) — 316 citations
• Direct Output Connection for a High-Rank Language Model (2018) — 37 citations
• Findings of the 2021 Conference on Machine Translation (WMT21) (2021) — 244 citations
• An Empirical Study of Building a Strong Baseline for Constituency Parsing (2018) — 16 citations
• Input-to-Output Gate to Improve RNN Language Models (2017) — 6 citations
• RNN-based Encoder-decoder Approach with Word Frequency Estimation (2017) — 13 citations
• Source-side Prediction for Neural Headline Generation (2017) — 9 citations
• Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing (2012) — 58 citations
• Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners (2011) — 221 citations
• Generalized Minimum Bayes Risk System Combination (2011) — 20 citations
• A Stochastic Japanese Morphological Analyzer Using a Forward-DP Backward-A* N-Best Search Algorithm (1994)