Shu Lih Oh

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 8 papers; trusts 8 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Cognitive Neuroscience — 39 citations into it; #2,838 of 10,852 by trust here, #1,628 by citations
• Cardiology and Cardiovascular Medicine — 32 citations into it; #80 of 906 by trust here, #128 by citations
• Signal Processing — 25 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Shu Lih Oh trusts overall

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

1. Samanwoy Ghosh-Dastidar
2. Hojjat Adeli
3. Diederik P. Kingma
4. Usha R. Acharya
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Hamido Fujita
8. Yann LeCun
9. Yoshua Bengio
10. Dan Claudiu Ciresan

Who trusts Shu Lih Oh overall

1. Dr. Prabira Kumar Sethy
2. Meghavi Rana
3. T. Kim
4. Santi Kumari Behera
5. Georgios Petmezas
6. Nagarajan Ganapathy
7. Usha R. Acharya
8. Megha Bhushan
9. Sung-Bae Cho
10. Özal Yıldırım

Papers from Shu Lih Oh's perspective

Ordered by how strongly the researchers Shu Lih Oh 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. Gradient-based learning applied to document 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. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network (2017) — 350 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
• Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats (2018) — 643 citations
• Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals (2018) — 331 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