Kevin Bauer

Artificial Intelligence · 2021–2023

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

Subfields

Whom Kevin Bauer trusts overall

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

  1. Scott M. Lundberg
  2. Su-In Lee
  3. Johanna D. Moore
  4. Finale Doshi-Velez

Who trusts Kevin Bauer overall

  1. Michael Ridley

Papers from Kevin Bauer's perspective

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

  1. Towards A Rigorous Science of Interpretable Machine Learning
  2. A Unified Approach to Interpreting Model Predictions
  3. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
  4. How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
  5. Explanation in Expert Systems : A Survey
  6. How AI detectives are cracking open the black box of deep learning
  7. How and What Can Humans Learn from Being in the Loop?
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. ImageNet classification with deep convolutional neural networks
  10. ImageNet Large Scale Visual Recognition Challenge

Their papers