Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions

9 February 2023

Papers citing "Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions"

10 / 10 papers shown

Title
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding T. Nathan Mundhenk Mikel Landajuela Ruben Glatt Claudio Santiago Daniel Faissol Brenden K. Petersen 68 91 0 29 Oct 2021
Symbolic Pregression: Discovering Physical Laws from Distorted Video S. Udrescu Max Tegmark 51 41 0 19 May 2020
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 120 940 0 20 Jun 2018
Learning Equations for Extrapolation and Control Subham S. Sahoo Christoph H. Lampert Georg Martius 38 233 0 19 Jun 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 120 437 0 28 May 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 276 2,257 0 24 Jun 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 164 3,865 0 10 Apr 2017
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 84 838 0 16 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 152 3,685 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 859 16,891 0 16 Feb 2016