Keita Kurita

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 96 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #11,889 of 35,345 by trust here, #7,112 by citations
• Natural Language Processing — 62 citations into it
• Safety, Robustness & Interpretability — 58 citations into it; #788 of 2,251 by trust here, #536 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Signal Processing — 19 citations into it
• AI Ethics, Fairness & Society — 10 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• General Social Sciences — 7 citations into it

Whom Keita Kurita trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Yann LeCun
6. Christian Szegedy
7. Tomas Mikolov
8. Alex Krizhevsky
9. Ian J. Goodfellow
10. Richard Socher

Who trusts Keita Kurita overall

1. Svetlana Kiritchenko
2. Fanchao Qi
3. Pieter Delobelle
4. Omar U. Florez
5. Percy Liang
6. Eric Wallace
7. Andrew Silva
8. João Sedoc
9. Wei Guo
10. Robert Munro

Papers from Keita Kurita's perspective

Ordered by how strongly the researchers Keita Kurita 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Measuring Bias in Contextualized Word Representations (2019) — 517 citations
• Weight Poisoning Attacks on Pretrained Models (2020) — 605 citations
• Quantifying Social Biases in Contextual Word Representations (2019) — 37 citations
• Towards Robust Toxic Content Classification (2019) — 41 citations