Ulrich Schaechtle

Probabilistic & Bayesian Methods · 2018

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Ulrich Schaechtle trusts overall

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

  1. Vikash K. Mansinghka
  2. David John Cameron MacKay
  3. Tom Minka
  4. Avi Pfeffer
  5. Lawrence M. Murray
  6. Martin J. Wainwright
  7. Dustin Tran
  8. Michael I. Jordan
  9. Radford M. Neal
  10. Geoffrey E. Hinton

Papers from Ulrich Schaechtle's perspective

Ordered by how strongly the researchers Ulrich Schaechtle 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. Gradient-based learning applied to document recognition
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. Long Short-Term Memory
  6. Learning Multiple Layers of Features from Tiny Images
  7. ImageNet: A large-scale hierarchical image database
  8. Attention is All you Need (Neural Information Processing Systems)
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. GENERATIVE ADVERSARIAL NETS

Their papers