Jay Busch

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jay Busch trusts

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

  1. Jitendra Malik (in Computer Vision and Pattern Recognition)
  2. Charles Ruizhongtai Qi (in 3D Vision & Pose)
  3. Jonathan T. Barron (in Computer Vision and Pattern Recognition)
  4. Kaiming He (in Computer Vision and Pattern Recognition)
  5. Alexei A. Efros (in Computer Vision and Pattern Recognition)
  6. Shaoqing Ren (in Computer Vision and Pattern Recognition)

Papers from Jay Busch's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  3. Backpropagation Applied to Handwritten Zip Code Recognition
  4. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  5. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
  6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  7. TensorFlow: A system for large-scale machine learning
  8. Image quality assessment: from error visibility to structural similarity
  9. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
  10. Gradient-based learning applied to document recognition

Their papers