Kuangyan Song

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Kuangyan Song trusts overall

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

  1. Sarah Tan
  2. Marco Tulio Ribeiro
  3. R. Caruana
  4. Giles Hooker
  5. Jerome H. Friedman
  6. A. Goldstein

Papers from Kuangyan Song's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. The mythos of model interpretability
  4. Gradient-based learning applied to document recognition
  5. Towards A Rigorous Science of Interpretable Machine Learning
  6. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
  7. Anchors: High-Precision Model-Agnostic Explanations
  8. Learning Important Features Through Propagating Activation Differences
  9. Greedy function approximation: A gradient boosting machine.
  10. Axiomatic Attribution for Deep Networks

Their papers