Kenta Oono

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 101 researchers and is trusted by 103 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Graph Learning — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Optimization & Training — 9 citations into it
• Model Architectures — 8 citations into it; #194 of 2,278 by trust here, #612 by citations
• Statistical and Nonlinear Physics — 7 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Scaling & Learning Theory — 6 citations into it

Whom Kenta Oono trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yann LeCun
5. Diederik P. Kingma
6. Yoshua Bengio
7. Oriol Vinyals
8. Matus Telgarsky
9. Tomas Mikolov
10. Nitish Srivastava

Who trusts Kenta Oono overall

1. Takaaki Hori
2. Shinji Watanabe
3. Sebastian Nowozin
4. Sosuke Kobayashi
5. Takeru Miyato
6. Masaki Saito
7. Takuya Akiba
8. Hiroshi Inoue
9. Eli Chien
10. Kazuki Osawa

Papers from Kenta Oono's perspective

Ordered by how strongly the researchers Kenta Oono 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Graph Neural Networks Exponentially Lose Expressive Power for Node Classification (2019) — 917 citations
• Chainer : a Next-Generation Open Source Framework for Deep Learning (2015) — 812 citations