Yuji Matsumoto

Artificial Intelligence · 2000–2020

A researcher in the Science corpus: 13 papers; trusts 148 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #3,817 of 35,345 by trust here, #6,614 by citations
• Natural Language Processing — 72 citations into it; #899 of 5,688 by trust here, #1,475 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #15,581 of 21,575 by trust here, #17,757 by citations
• Graph Learning — 18 citations into it
• Optimization & Training — 16 citations into it; #684 of 2,601 by trust here, #1,313 by citations
• Model Architectures — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Safety, Robustness & Interpretability — 8 citations into it; #1,639 of 2,251 by trust here, #2,041 by citations

Whom Yuji Matsumoto trusts overall

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

1. Robert E. Schapire
2. Michael Collins
3. Yoav Freund
4. Leo Breiman
5. David Haussler
6. Vladimir Naumovich Vapni
7. Vladimir Naumovich Vapnik
8. Geoffrey E. Hinton
9. Diederik P. Kingma
10. Andrew McCallum

Who trusts Yuji Matsumoto overall

1. Joakim Nivre
2. Ryan T. McDonald
3. Yue Zhang
4. Richard Johansson
5. Sameer Pradhan
6. Simone Paolo Ponzetto
7. Fernando C Pereira
8. Noah A. Smith
9. Tao Ge
10. Yoav Goldberg

Papers from Yuji Matsumoto's perspective

Ordered by how strongly the researchers Yuji Matsumoto 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. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• Chunking with Support Vector Machines (2001) — 598 citations
• Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach (2017) — 379 citations
• Use of Support Vector Learning for Chunk Identification (2000) — 347 citations
• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020) — 780 citations
• Interpretable Adversarial Perturbation in Input Embedding Space for Text (2018) — 208 citations
• Universal Dependencies 2.1 (2017) — 425 citations
• A* CCG Parsing with a Supertag and Dependency Factored Model (2017) — 69 citations
• Mining Revision Log of Language Learning SNS for Automated Japanese Error Correction of Second Language Learners (2011) — 221 citations
• An Application of Boosting to Graph Classification (2004) — 206 citations
• Applying Conditional Random Fields to Japanese Morphological Analysis (2004) — 950 citations
• A Boosting Algorithm for Classification of Semi-Structured Text (2004) — 164 citations
• Statistical Dependency Analysis with Support Vector Machines (2003) — 728 citations
• Japanese Dependency Analysis using Cascaded Chunking (2002) — 569 citations