Young-Joon Kim

Artificial Intelligence · with Kim · 2017

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 7 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Young-Joon Kim trusts overall

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

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

Who trusts Young-Joon Kim overall

1. M. Mohammadi
2. Yuchen Jiang

Papers from Young-Joon Kim's perspective

Ordered by how strongly the researchers Young-Joon 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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