Optimizing for Interpretability in Deep Neural Networks with Tree
Regularization

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

14 August 2019

Finale Doshi-Velez

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Papers citing "Optimizing for Interpretability in Deep Neural Networks with Tree Regularization"

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Title
NIMO: a Nonlinear Interpretable MOdel Shijian Xu M. Negri Volker Roth FAtt 95 0 0 05 Jun 2025
Smooth InfoMax -- Towards Easier Post-Hoc Interpretability Fabian Denoodt Bart de Boer José Oramas 169 2 0 23 Aug 2024
3VL: Using Trees to Improve Vision-Language Models' Interpretability Nir Yellinek Leonid Karlinsky Raja Giryes CoGe VLM 287 3 0 28 Dec 2023
Variational Information Pursuit for Interpretable Predictions Aditya Chattopadhyay Kwan Ho Ryan Chan B. Haeffele D. Geman René Vidal DRL 99 14 0 06 Feb 2023
Interpreting Neural Policies with Disentangled Tree Representations Tsun-Hsuan Wang Wei Xiao Tim Seyde Ramin Hasani Daniela Rus DRL 104 2 0 13 Oct 2022
Interpretable Deep Tracking Benjamin Thérien Krzysztof Czarnecki 84 0 0 03 Oct 2022
A Survey of Neural Trees Haoling Li Mingli Song Mengqi Xue Haofei Zhang Jingwen Ye Lechao Cheng Mingli Song AI4CE 95 6 0 07 Sep 2022
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 90 88 0 21 Oct 2020
SIDU: Similarity Difference and Uniqueness Method for Explainable AI Satya M. Muddamsetty M. N. Jahromi T. Moeslund 39 11 0 04 Jun 2020
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models Benjamin J. Lengerich S. Tan C. Chang Giles Hooker R. Caruana 68 42 0 12 Nov 2019