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

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

8 / 8 papers shown

Title
Smooth InfoMax -- Towards easier Post-Hoc interpretability Fabian Denoodt Bart de Boer José Oramas 21 2 0 23 Aug 2024
3VL: Using Trees to Improve Vision-Language Models' Interpretability Nir Yellinek Leonid Karlinsky Raja Giryes CoGe VLM 54 4 0 28 Dec 2023
Variational Information Pursuit for Interpretable Predictions Aditya Chattopadhyay Kwan Ho Ryan Chan B. Haeffele D. Geman René Vidal DRL 29 11 0 06 Feb 2023
SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report] Hamed Ayoobi Nico Potyka Francesca Toni 26 18 0 23 Jan 2023
Interpretable Deep Tracking Benjamin Thérien Krzysztof Czarnecki 36 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 25 6 0 07 Sep 2022
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 24 85 0 21 Oct 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017