Matthew Klingensmith

Computer Vision and Pattern Recognition · 2013–2015

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Matthew Klingensmith trusts overall

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

  1. Brian Curless
  2. Nathan D. Ratliff
  3. Jiawen Chen
  4. Matthias Nießner
  5. Jürgen Sturm
  6. Thomas Whelan
  7. Matthias Teschner
  8. Kai M. Wurm
  9. Marc Levoy
  10. Shahram Izadi

Papers from Matthew Klingensmith's perspective

Ordered by how strongly the researchers Matthew Klingensmith 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  4. A volumetric method for building complex models from range images
  5. Gradient-based learning applied to document recognition
  6. A Method for Registration of 3-D Shapes
  7. Parallel Tracking and Mapping for Small AR Workspaces
  8. ImageNet: A large-scale hierarchical image database
  9. A benchmark for the evaluation of RGB-D SLAM systems
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers