Claire McQuin

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Claire McQuin trusts overall

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

  1. Ross B. Girshick
  2. Serge Beucher
  3. Kaiming He
  4. Jitendra Malik

Who trusts Claire McQuin overall

  1. Erick Moen
  2. Juan C. Caicedo

Papers from Claire McQuin's perspective

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

  1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  2. Rectified Linear Units Improve Restricted Boltzmann Machines
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Fully convolutional networks for semantic segmentation
  5. This PDF file includes: Materials and Methods
  6. ImageNet classification with deep convolutional neural networks
  7. Human-level control through deep reinforcement learning
  8. Mask R-CNN
  9. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  10. Gradient-based learning applied to document recognition

Their papers