Laura Beggel

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Laura Beggel trusts

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

  1. Xavier Gastaldi (in Computer Vision and Pattern Recognition)
  2. Kaiming He (in Computer Vision and Pattern Recognition)
  3. Ross B. Girshick (in Computer Vision and Pattern Recognition)

Papers from Laura Beggel's perspective

Ordered by how strongly the researchers Laura Beggel 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. Aggregated Residual Transformations for Deep Neural Networks
  3. Automatic differentiation in PyTorch
  4. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
  5. Temporal Ensembling for Semi-Supervised Learning
  6. ImageNet classification with deep convolutional neural networks
  7. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
  8. Learning Multiple Layers of Features from Tiny Images
  9. Gradient-based learning applied to document recognition
  10. Learning to Reweight Examples for Robust Deep Learning

Their papers