Thomas Probst

Computer Vision and Pattern Recognition · with Gool · 2016

A researcher in the Science corpus: 1 paper; trusts 39 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Thomas Probst trusts overall

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

  1. Zhiwu Huang
  2. L. Eon Bottou
  3. David William Pearson
  4. Raviteja Vemulapalli
  5. Meinard Müller
  6. Ramalingam Chellappa
  7. Silvere Bonnabel
  8. Pavan K. Turaga
  9. Sam T. Roweis
  10. Geoffrey E. Hinton

Who trusts Thomas Probst overall

  1. Hossein Rahmani
  2. Mengyuan Liu

Papers from Thomas Probst's perspective

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

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. ImageNet classification with deep convolutional neural networks
  3. Gradient-based learning applied to document recognition
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. ImageNet: A large-scale hierarchical image database
  6. Long Short-Term Memory
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  9. ImageNet Large Scale Visual Recognition Challenge
  10. Going deeper with convolutions

Their papers