Kuniaki Uehara

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 5 papers; trusts 530 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #17,462 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #11,340 of 21,575 by trust here, #14,151 by citations
• Model Architectures — 30 citations into it
• Optimization & Training — 22 citations into it
• Management Science and Operations Research — 21 citations into it; #3,158 of 11,946 by trust here, #4,118 by citations
• Natural Language Processing — 11 citations into it
• Time-Series & Forecasting — 10 citations into it; #80 of 381 by trust here, #124 by citations
• Representation & Self-Supervised Learning — 8 citations into it

Whom Kuniaki Uehara trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yoshua Bengio
7. Yann LeCun
8. Jimmy Ba
9. Christian Szegedy
10. Sergey Ioffe

Who trusts Kuniaki Uehara overall

1. Omer Berat Sezer
2. A. Ozbayoglu
3. M. Ugur Gudelek
4. Weiwei Jiang
5. Sylvestre-Alvise Rebuffi
6. Takashi Matsubara
7. Maithra Raghu

Papers from Kuniaki Uehara's perspective

Ordered by how strongly the researchers Kuniaki Uehara 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. 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. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Data Augmentation Using Random Image Cropping and Patching for Deep CNNs (2018) — 406 citations
• RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs (2018) — 131 citations
• Deep learning for stock prediction using numerical and textual information (2016) — 347 citations
• Predicting Stock Market Trends by Recurrent Deep Neural Networks (2014) — 86 citations
• Stock Price Prediction by Deep Neural Generative Model of News Articles (2018) — 51 citations