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Avishek Anand
interpretability
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32655383
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Avishek Anand
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@@ -22,19 +22,19 @@ We release [InterpretMe]
*Ruth C Fong and Andrea Vedaldi.*
.CVPR 2017. [paper]
1.
**Supervised topic models for clinical interpretability.**
*Michael C Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, and Finale Doshi-Velez
.
*
.2016.
[
paper
](
https://arxiv.org/pdf/1612.01678
)
*Michael C Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, and Finale Doshi-Velez*
.2016.
[
paper
](
https://arxiv.org/pdf/1612.01678
)
1.
**A unified approach to interpreting model predictions.**
*Scott Lundberg and Su-In Lee
.
*
.2016.
[
paper
](
https://arxiv.org/pdf/1705.07874
)
*Scott Lundberg and Su-In Lee*
.2016.
[
paper
](
https://arxiv.org/pdf/1705.07874
)
1.
**A human-grounded evaluation benchmark for local explanations of machine learning.**
*Sina Mohseni and Eric D Ragan
.
*
.2018.
[
paper
](
https://arxiv.org/pdf/1801.05075
)
.
*Sina Mohseni and Eric D Ragan*
.2018.
[
paper
](
https://arxiv.org/pdf/1801.05075
)
.
1.
**Anchors: High-precision model-agnostic explanations.**
*Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin
.
*
.AAAI 2018. [paper]
*Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin*
.AAAI 2018. [paper]
1.
**Right for the right reasons: Training differentiable models by constraining their explanations.**
*Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez
.
*
.IJCAI 2018.
[
paper
](
https://doi.org/10.24963/ijcai.2017/371
)
*Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez*
.IJCAI 2018.
[
paper
](
https://doi.org/10.24963/ijcai.2017/371
)
1.
**Sharing Deep Neural Network Models with Interpretation.**
*Huijun Wu, Chen Wang, Jie Yin, Kai Lu and Liming Zhu*
. WWW’18.
[
paper
](
https://doi.org/10.24963/ijcai.2017/371
)
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