Kentaro Inui
Artificial Intelligence · 2007–2022
A researcher in the Science corpus: 13 papers; trusts 437 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 260 citations into it; #8,356 of 35,345 by trust here, #6,569 by citations
- Natural Language Processing — 231 citations into it; #1,383 of 5,688 by trust here, #1,779 by citations
- Computer Vision and Pattern Recognition — 100 citations into it; #14,559 of 21,575 by trust here, #9,722 by citations
- Evaluation & Benchmarks — 89 citations into it
- Large Language Models — 41 citations into it; #491 of 799 by trust here, #746 by citations
- Model Architectures — 30 citations into it
- Information Retrieval & Search — 19 citations into it
- Representation & Self-Supervised Learning — 14 citations into it
Whom Kentaro Inui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Ilya Sutskever
- Geoffrey E. Hinton
- Diederik P. Kingma
- Sepp Hochreiter
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
- J. Urgen Schmidhuber
- Kaiming He
Who trusts Kentaro Inui overall
- Richard Socher
- Yongfeng Zhang
- Yoon Kim
- Jordan L. Boyd-Graber
- Christopher D. Manning
- Patrick Lewis
- Tara Safavi
- Sida I. Wang
- Karl Moritz Hermann
- Stefan Harmeling
Papers from Kentaro Inui's perspective
Ordered by how strongly the researchers Kentaro Inui trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- 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)
- 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
- What Makes Reading Comprehension Questions Easier? (2018) — 127 citations
- Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets (2019) — 92 citations
- When Choosing Plausible Alternatives, Clever Hans can be Clever (2019) — 63 citations
- RealTime QA: What's the Answer Right Now? (2022) — 301 citations
- Dynamic Entity Representation with Max-pooling Improves Machine Reading (2016) — 41 citations
- SHAPE: Shifted Absolute Position Embedding for Transformers (2021) — 60 citations
- Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2020) — 156 citations
- Can Neural Networks Understand Monotonicity Reasoning? (2019) — 89 citations
- Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing (2007) — 81 citations
- Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020) — 163 citations
- Handling Multiword Expressions in Causality Estimation (2017) — 29 citations
- Source-side Prediction for Neural Headline Generation (2017) — 9 citations
- Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables (2010) — 383 citations