John G. Oberlin

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom John G. Oberlin trusts overall

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

  1. David G. Lowe
  2. Richard Szeliski
  3. John F. Canny
  4. Shie Mannor
  5. Oded Maron
  6. Sébastien Bubeck
  7. John N. Tsitsiklis
  8. Andrew W. Moore
  9. Sebastian Thrun
  10. R´emi Munos

Who trusts John G. Oberlin overall

  1. Jeffrey Mahler
  2. Sergey Levine
  3. Peter Pastor
  4. Jacky Liang
  5. Florian T. Pokorny
  6. Michael Laskey
  7. Alex Krizhevsky

Papers from John G. Oberlin's perspective

Ordered by how strongly the researchers John G. Oberlin 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. ImageNet: A large-scale hierarchical image database
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Long Short-Term Memory
  7. Learning Multiple Layers of Features from Tiny Images
  8. Attention is All you Need (Neural Information Processing Systems)
  9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers