Joseph Y. Lo

Computer Vision and Pattern Recognition · 2008–2021

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Joseph Y. Lo trusts

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

  1. Andrew P. Bradley (in Evaluation & Benchmarks)
  2. Nancy A. Obuchowski (in Epidemiology)
  3. Karen Simonyan (in Computer Vision and Pattern Recognition)
  4. Andrew Zisserman (in Computer Vision and Pattern Recognition)

Papers from Joseph Y. Lo's perspective

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

  1. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. ImageNet classification with deep convolutional neural networks
  4. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
  5. The use of the area under the ROC curve in the evaluation of machine learning algorithms
  6. Sanity Checks for Saliency Maps
  7. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  8. Gradient-based learning applied to document recognition
  9. Receiver operating characteristic curves and their use in radiology. (Radiology)
  10. This looks like that: deep learning for interpretable image recognition

Their papers