Unwrapping The Black Box of Deep ReLU Networks: Interpretability,
Diagnostics, and Simplification

Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification

8 November 2020

Papers citing "Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification"

11 / 11 papers shown

Title
Modular Training of Neural Networks aids Interpretability Satvik Golechha Maheep Chaudhary Joan Velja Alessandro Abate Nandi Schoots 79 0 0 04 Feb 2025
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 94 32 0 29 Apr 2023
The Power of Typed Affine Decision Structures: A Case Study Gerrit Nolte Maximilian Schlüter Alnis Murtovi Bernhard Steffen AAML 20 3 0 28 Apr 2023
LEURN: Learning Explainable Univariate Rules with Neural Networks Çağlar Aytekin FAtt 29 0 0 27 Mar 2023
On marginal feature attributions of tree-based models Khashayar Filom A. Miroshnikov Konstandinos Kotsiopoulos Arjun Ravi Kannan FAtt 22 3 0 16 Feb 2023
Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations Maximilian Schlüter Gerrit Nolte Alnis Murtovi Bernhard Steffen 29 6 0 19 Jan 2023
Neural Networks are Decision Trees Çağlar Aytekin FAtt 32 24 0 11 Oct 2022
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death Joshua C. Chang Ted L. Chang Carson C. Chow R. Mahajan Sonya Mahajan Joe Maisog Shashaank Vattikuti Hongjing Xia FAtt OOD 37 0 0 28 Aug 2022
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models Linwei Hu Jie Chen V. Nair 36 3 0 14 Jul 2022
Designing Inherently Interpretable Machine Learning Models Agus Sudjianto Aijun Zhang FaML 11 31 0 02 Nov 2021
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 240 1,837 0 03 Feb 2017