Young-Kil Kim

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Young-Kil Kim trusts overall

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

  1. Diederik P. Kingma
  2. Sosuke Kobayashi
  3. Bing Liu
  4. Xiaodong Zhang
  5. Jimmy Ba
  6. Geoffrey E. Hinton
  7. Yoshua Bengio
  8. Mohit Iyyer

Papers from Young-Kil Kim's perspective

Ordered by how strongly the researchers Young-Kil Kim trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  2. ImageNet classification with deep convolutional neural networks
  3. Long Short-Term Memory
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Attention is All you Need (Neural Information Processing Systems)
  6. Gradient-based learning applied to document recognition
  7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
  8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
  9. GENERATIVE ADVERSARIAL NETS
  10. ImageNet: A large-scale hierarchical image database

Their papers