Akira Yoshihara

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #20,949 of 35,345 by trust here, #25,040 by citations
• Management Science and Operations Research — 12 citations into it; #3,747 of 11,946 by trust here, #4,602 by citations
• Time-Series & Forecasting — 6 citations into it; #100 of 381 by trust here, #129 by citations
• Model Architectures — 5 citations into it

Whom Akira Yoshihara trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. J. Urgen Schmidhuber
4. Ilya Sutskever
5. Geoffrey E. Hinton
6. Wojciech Zaremba
7. Felix Alexander Gers
8. Marc-André Mittermayer
9. Yoshua Bengio
10. Jimmy Ba

Who trusts Akira Yoshihara overall

1. Omer Berat Sezer
2. A. Ozbayoglu
3. M. Ugur Gudelek
4. Takashi Matsubara
5. Weiwei Jiang
6. Ryo Akita
7. Liheng Zhang
8. Bang Xiang Yong
9. Khaled A. Althelaya
10. Kuniaki Uehara

Papers from Akira Yoshihara's perspective

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

1. Long Short-Term Memory
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• 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