11 citations · 27 across the 7 of their papers we have counts for
4 papers · 1 filter
Adaptive wavelet distillation from neural networks through interpretations
Wooseok Ha, Chandan Singh, Francois Lanusse +2
Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many…
Interpreting and improving deep-learning models with reality checks
Chandan Singh, Wooseok Ha, Bin Yu
Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter cov…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier +2
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for pre…