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Variable selection with false discovery rate control in deep neural networks

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Zixuan Song, Jun Li

Machine Learning · Submitted 17 Sep 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control.

how it is stated · asserted 1.00 read the record →

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arXiv ID1909.07561 (version 1)
Pith IDDS4627BC, Pith's permanent record of this paper and its review
CitationJun Li, Zixuan Song. "Variable selection with false discovery rate control in deep neural networks." arXiv:1909.07561 (2019). https://pith.science/paper/DS4627BC
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The compiled record

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,889 input tokens.

What the abstract itself states, as probabilities: claims first 0.60 · states limitation 0.09 · human participants 0.10 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.53 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. background 1.00 0.03 0.02 asserted 1.00
s1 We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. background 0.94 0.22 0.03 asserted 0.72
s2 We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. method 0.55 0.94 0.12 asserted 1.00
s3 More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. contribution 0.90 0.81 0.60 asserted 1.00
s4 Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. method 0.77 0.86 0.07 asserted 1.00
s5 To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets. method 0.82 0.93 0.05 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.73.

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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in DNNs. We propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables, while no existing methods provide such a quality control. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. To study its validity, SurvNet is applied to image data and gene expression data, as well as various simulation datasets.

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