Kazuya Kawakami

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 93 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #4,842 of 35,345 by trust here, #4,254 by citations
• Natural Language Processing — 65 citations into it; #1,143 of 5,688 by trust here, #904 by citations
• Representation & Self-Supervised Learning — 33 citations into it; #2,060 of 3,145 by trust here, #1,943 by citations
• Speech & Audio — 24 citations into it; #889 of 1,076 by trust here, #1,043 by citations
• Signal Processing — 20 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Model Architectures — 11 citations into it

Whom Kazuya Kawakami trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Chris Dyer
4. Geoffrey E. Hinton
5. Alex Graves
6. Tomas Mikolov
7. Ilya Sutskever
8. Richard Socher
9. Philipp Koehn
10. Ronan Collobert

Who trusts Kazuya Kawakami overall

1. Alexei Baevski
2. Abdel-rahman Mohamed
3. Luyu Wang
4. Morgane Rivière
5. Yoav Goldberg
6. Michael Auli
7. Tu Nguyen
8. Matthew E. Peters
9. Young-Bum Kim
10. Aäron van den Oord

Papers from Kazuya Kawakami's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Neural Architectures for Named Entity Recognition (2016) — 4,356 citations
• Learning to Represent Words in Context with Multilingual Supervision (2015) — 16 citations
• Learning Robust and Multilingual Speech Representations (2020) — 103 citations
• Unsupervised Learning of Efficient and Robust Speech Representations (2019) — 7 citations