Masahiro Kaneko

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 65 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Natural Language Processing — 36 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Masahiro Kaneko trusts overall

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

1. Tomas Mikolov
2. Diederik P. Kingma
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jieyu Zhao
7. Richard Socher
8. Jimmy Ba
9. Daniel Dahlmeier
10. Jeffrey Pennington

Who trusts Masahiro Kaneko overall

1. Tianlu Wang
2. Zhaohong Wan
3. Dawn Drain
4. Yu Wang
5. Emily Dinan
6. Enoch Opanin Gyamfi
7. Xi Victoria Lin
8. Xiaojun Wan

Papers from Masahiro Kaneko's perspective

Ordered by how strongly the researchers Masahiro Kaneko trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Gender-preserving Debiasing for Pre-trained Word Embeddings (2019) — 138 citations
• Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020) — 163 citations