Malin Eiband

Artificial Intelligence · 2021

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 Malin Eiband trusts overall

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

  1. Cynthia Rudin
  2. Lloyd S. Shapley
  3. Shubham Rathi
  4. Robert R. Hoffman
  5. Shane T. Mueller
  6. Leon Rozenblit
  7. Alicja Gosiewska
  8. Marco Tulio Ribeiro
  9. Danding Wang

Papers from Malin Eiband's perspective

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

  1. The mythos of model interpretability
  2. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
  3. Explanation in Artificial Intelligence: Insights from the Social Sciences
  4. ImageNet classification with deep convolutional neural networks
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  7. The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems (2015 International Conference on Healthcare Informatics)
  8. Long Short-Term Memory
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. Gradient-based learning applied to document recognition

Their papers