Markus Eisenbach

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Markus Eisenbach trusts overall

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

  1. Christian Szegedy
  2. Kaiming He
  3. Alex Krizhevsky
  4. Sergey Ioffe
  5. Geoffrey E. Hinton
  6. Karen Simonyan
  7. Ilya Sutskever
  8. X. Zhang
  9. Andrew Zisserman
  10. Shaoqing Ren

Papers from Markus Eisenbach's perspective

Ordered by how strongly the researchers Markus Eisenbach 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. Long Short-Term Memory
  5. Gradient-based learning applied to document recognition
  6. Attention is All you Need (Neural Information Processing Systems)
  7. ImageNet: A large-scale hierarchical image database
  8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Going deeper with convolutions

Their papers