Jan Pfeifer

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jan Pfeifer trusts overall

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

  1. Trevor J. Hastie
  2. Eric R. Ziegel
  3. Thorsten Joachims
  4. Tin Kam Ho

Who trusts Jan Pfeifer overall

  1. Andrew Cotter
  2. Maya R. Gupta

Papers from Jan Pfeifer's perspective

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

  1. Et al
  2. Dropout: a simple way to prevent neural networks from overfitting
  3. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
  4. Rectified Linear Units Improve Restricted Boltzmann Machines
  5. The Elements of Statistical Learning
  6. Automatic differentiation in PyTorch
  7. ImageNet classification with deep convolutional neural networks
  8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  9. Representation Learning: A Review and New Perspectives
  10. Gradient-based learning applied to document recognition

Their papers