Minseok Kim

Artificial Intelligence · with Lee · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 170 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it
• Optimization & Training — 47 citations into it
• Safety, Robustness & Interpretability — 31 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Statistical Learning Theory — 10 citations into it
• Model Architectures — 5 citations into it
• Natural Language Processing — 5 citations into it

Whom Minseok Kim trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Ilya Sutskever
5. Sergey Ioffe
6. Yoshua Bengio
7. Radford M. Neal
8. Kaiming He
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Minseok Kim overall

1. Hwanjun Song
2. Steven Euijong Whang
3. Sheng Liu
4. Pan Zhang
5. Haiyang Zhang

Papers from Minseok Kim's perspective

Ordered by how strongly the researchers Minseok Kim 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning From Noisy Labels With Deep Neural Networks: A Survey (2020) — 1,409 citations
• Robust Learning by Self-Transition for Handling Noisy Labels (2020) — 46 citations
• SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (2019) — 467 citations