David R. J. Snead

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom David R. J. Snead trusts overall

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

  1. Radford M. Neal
  2. Alex Krizhevsky

Who trusts David R. J. Snead overall

  1. Anant Madabhushi

Papers from David R. J. Snead's perspective

Ordered by how strongly the researchers David R. J. Snead trusts endorse each paper. Same corpus, a different order for every researcher.

  1. ImageNet classification with deep convolutional neural networks
  2. Dropout: a simple way to prevent neural networks from overfitting
  3. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  4. Pattern Recognition and Machine Learning
  5. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
  6. Gradient-based learning applied to document recognition
  7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  8. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
  9. Learning Multiple Layers of Features from Tiny Images
  10. ImageNet Large Scale Visual Recognition Challenge

Their papers