Sungjin Won

Computational Mechanics · 2020

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

Whom Sungjin Won trusts overall

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

  1. Diederik P. Kingma
  2. J. Nathan Kutz
  3. A. W. Vreman
  4. Mehdi Mirza
  5. Geoffrey E. Hinton
  6. Thomas Kipf
  7. Jimmy Ba

Papers from Sungjin Won's perspective

Ordered by how strongly the researchers Sungjin Won 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Gradient-based learning applied to document recognition
  6. Long Short-Term Memory
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Learning Multiple Layers of Features from Tiny Images
  9. Going deeper with convolutions
  10. ImageNet: A large-scale hierarchical image database

Their papers