Jae H. Kim

Computer Vision and Pattern Recognition · with Liu · 2020

A researcher in the Science corpus: 1 paper; trusts 90 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jae H. Kim trusts overall

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

  1. Ross B. Girshick
  2. Joseph Redmon
  3. Kaiming He
  4. Christian Szegedy
  5. Tsung-Yi Lin
  6. Alex Krizhevsky
  7. Shaoqing Ren
  8. Geoffrey E. Hinton
  9. Sergey Ioffe
  10. Karen Simonyan

Papers from Jae H. Kim's perspective

Ordered by how strongly the researchers Jae H. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  5. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. Microsoft COCO: Common Objects in Context
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. Long Short-Term Memory

Their papers