Consistent Feature Selection for Analytic Deep Neural Networks

Abstract

One of the most important steps toward interpretability and explainability of neural network models is feature selection, which aims to identify the subset of relevant features. Theoretical results in the field have mostly focused on the prediction aspect of the problem with virtually no work on feature selection consistency for deep neural networks due to the model's severe nonlinearity and unidentifiability. This lack of theoretical foundation casts doubt on the applicability of deep learning to contexts where correct interpretations of the features play a central role.

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Text

Dinh and Ho. "Consistent Feature Selection for Analytic Deep Neural Networks." Neural Information Processing Systems, 2020.

Markdown

[Dinh and Ho. "Consistent Feature Selection for Analytic Deep Neural Networks." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/dinh2020neurips-consistent/)

BibTeX

@inproceedings{dinh2020neurips-consistent,
  title     = {{Consistent Feature Selection for Analytic Deep Neural Networks}},
  author    = {Dinh, Vu C and Ho, Lam S},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/dinh2020neurips-consistent/}
}