Yuki Hagiwara

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 8 papers; trusts 24 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it
• Cognitive Neuroscience — 46 citations into it; #3,530 of 10,852 by trust here, #1,346 by citations
• Computer Vision and Pattern Recognition — 41 citations into it
• Cardiology and Cardiovascular Medicine — 25 citations into it; #120 of 906 by trust here, #114 by citations
• Signal Processing — 24 citations into it
• Electrical and Electronic Engineering — 8 citations into it
• Molecular Biology — 8 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Yuki Hagiwara trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Hojjat Adeli
6. Alex Krizhevsky
7. Samanwoy Ghosh-Dastidar
8. Usha R. Acharya
9. Ruslan Salakhutdinov
10. Kaiming He

Who trusts Yuki Hagiwara overall

1. Nagarajan Ganapathy
2. Dr. Prabira Kumar Sethy
3. Poomipat Boonyakitanont
4. Usha R. Acharya
5. Shu Lih Oh
6. Özal Yıldırım
7. Oliver Faust

Papers from Yuki Hagiwara's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Deep learning for healthcare applications based on physiological signals: A review (2018) — 935 citations
• A deep learning approach for Parkinson’s disease diagnosis from EEG signals (2018) — 571 citations
• Automated EEG-based screening of depression using deep convolutional neural network (2018) — 526 citations
• Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals (2018) — 331 citations
• Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network (2017) — 698 citations
• Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals (2017) — 1,449 citations
• A deep convolutional neural network model to classify heartbeats (2017) — 1,287 citations
• Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals (2017) — 842 citations