Erik Schaffernicht

Computer Vision and Pattern Recognition · 2015–2016

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

Subfields

Whom Erik Schaffernicht trusts overall

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

  1. Thomas G. Dietterich
  2. Andrew William Fitzgibbon
  3. John C. Hart
  4. Matthew A. Turk
  5. Michal Aharon
  6. Tanner Schmidt

Papers from Erik Schaffernicht's perspective

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

  1. This PDF file includes: Materials and Methods
  2. Scikit-learn: Machine Learning in Python
  3. Learning representations by back-propagating errors
  4. ImageNet classification with deep convolutional neural networks
  5. KinectFusion: Real-time dense surface mapping and tracking
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Robust Registration of 2D and 3D Point Sets (British Machine Vision Conference)
  8. Gradient-based learning applied to document recognition
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation (IEEE Transactions on Signal Processing)

Their papers