Chang Ouk Kim

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it

Whom Chang Ouk Kim trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Pascal Vincent
5. Yann LeCun
6. Ilya Sutskever
7. Nitish Srivastava
8. J. Urgen Schmidhuber
9. Hugo Larochelle
10. Ruslan Salakhutdinov

Papers from Chang Ouk Kim's perspective

Ordered by how strongly the researchers Chang Ouk Kim 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. Dropout: a simple way to prevent neural networks from overfitting
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise (2017) — 98 citations
• A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes (2016) — 390 citations