Variational Method

2019

A researcher in the Science corpus: 1 paper; trusts 0 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Whom Variational Method trusts overall

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

  1. Stanley J. Osher
  2. Ivan P. Gavrilyuk
  3. Tony F. Chan

Who trusts Variational Method overall

  1. Richard Nock
  2. Shusen Wang
  3. Han Lin Shang
  4. B. E. Hansen
  5. John C. Duchi
  6. Luca Marchesotti
  7. Matteo Barigozzi
  8. Jinzhu Jia
  9. Anders Gorst-rasmussen
  10. Satoshi Hara

Papers from Variational Method's perspective

Ordered by how strongly the researchers Variational Method 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. Snakes: Active contour models
  4. Gradient-based learning applied to document recognition
  5. Nonlinear total variation based noise removal algorithms
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  8. Long Short-Term Memory
  9. ImageNet: A large-scale hierarchical image database
  10. Learning Multiple Layers of Features from Tiny Images

Their papers