Minz Won

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Minz Won trusts overall

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

  1. Keunwoo Choi
  2. Brian McFee
  3. Diederik P. Kingma
  4. Kaiming He
  5. Dmitry Bogdanov
  6. Edith Law
  7. György Fazekas
  8. Jacob Devlin
  9. Jimmy Ba
  10. Jesse Davis

Who trusts Minz Won overall

  1. Eduardo Fonseca
  2. Andrés Ferraro
  3. Alexandre D'efossez

Papers from Minz Won's perspective

Ordered by how strongly the researchers Minz Won 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  3. ImageNet classification with deep convolutional neural networks
  4. Long Short-Term Memory
  5. Gradient-based learning applied to document recognition
  6. ImageNet: A large-scale hierarchical image database
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  10. ImageNet Large Scale Visual Recognition Challenge

Their papers