Takashi Matsubara

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 527 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #17,461 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #11,339 of 21,575 by trust here, #14,151 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 21 citations into it
• Management Science and Operations Research — 17 citations into it; #4,472 of 11,946 by trust here, #6,024 by citations
• Safety, Robustness & Interpretability — 8 citations into it
• Time-Series & Forecasting — 8 citations into it; #130 of 381 by trust here, #173 by citations
• Natural Language Processing — 7 citations into it

Whom Takashi Matsubara trusts overall

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

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

Who trusts Takashi Matsubara overall

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek
4. Weiwei Jiang
5. Sylvestre-Alvise Rebuffi
6. Maithra Raghu
7. Yoshihiro Yamada
8. Sven Gowal

Papers from Takashi Matsubara's perspective

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

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
• Stock Price Prediction by Deep Neural Generative Model of News Articles (2018) — 51 citations