Chaehun Shin

Generative Models · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 124 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Chaehun Shin trusts overall

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

  1. Diederik P. Kingma
  2. Geoffrey E. Hinton
  3. Yoshua Bengio
  4. Alex Krizhevsky
  5. Tero Karras
  6. Kaiming He
  7. Jonathan Ho
  8. Aäron van den Oord
  9. Yang Song
  10. Yann LeCun

Papers from Chaehun Shin's perspective

Ordered by how strongly the researchers Chaehun Shin trusts endorse each paper. Same corpus, a different order for every researcher.

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