Peter Schaldenbrand

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Peter Schaldenbrand trusts overall

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

  1. Karen Simonyan
  2. Kevin Frans
  3. Nicholas I. Kolkin
  4. Tzu-Mao Li
  5. Aditya Ramesh
  6. Alec Radford
  7. Andrew Zisserman
  8. Jason Salavon
  9. Lisa B. Soros
  10. Michal Lukác

Papers from Peter Schaldenbrand's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Gradient-based learning applied to document recognition
  5. ImageNet: A large-scale hierarchical image database
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Long Short-Term Memory
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Learning Multiple Layers of Features from Tiny Images

Their papers