Tim Becker

Computer Vision and Pattern Recognition · 2011–2019

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

Subfields

Whom Tim Becker trusts overall

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

  1. Xiaowei Chen
  2. Kaiming He

Papers from Tim Becker's perspective

Ordered by how strongly the researchers Tim Becker 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. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Rectified Linear Units Improve Restricted Boltzmann Machines
  4. ImageNet classification with deep convolutional neural networks
  5. Fully convolutional networks for semantic segmentation
  6. This PDF file includes: Materials and Methods
  7. Mask R-CNN
  8. Human-level control through deep reinforcement learning
  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