Christine Tanner

Computer Vision and Pattern Recognition · 2003–2018

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Christine Tanner trusts overall

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

  1. Sergey Ioffe
  2. Olaf Ronneberger
  3. Hannes Nickisch
  4. Nitish Srivastava
  5. Geoffrey E. Hinton
  6. Christian Szegedy
  7. Alex Krizhevsky

Who trusts Christine Tanner overall

  1. Torsten Rohlfing
  2. Mark Holden
  3. Francisco P. M. Oliveira
  4. Marco Forte

Papers from Christine Tanner's perspective

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

  1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  2. ImageNet classification with deep convolutional neural networks
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Dropout: a simple way to prevent neural networks from overfitting
  5. Gradient-based learning applied to document recognition
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Going deeper with convolutions
  8. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers