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Rule Extraction Algorithm for Deep Neural Networks: A Review

Rule Extraction Algorithm for Deep Neural Networks: A Review

16 September 2016
Tameru Hailesilassie
ArXivPDFHTML

Papers citing "Rule Extraction Algorithm for Deep Neural Networks: A Review"

10 / 10 papers shown
Title
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
Giovanni Ciatto
Federico Sabbatini
Andrea Agiollo
Matteo Magnini
Andrea Omicini
46
14
0
28 Jan 2025
Automating the Design and Development of Gradient Descent Trained Expert
  System Networks
Automating the Design and Development of Gradient Descent Trained Expert System Networks
Jeremy Straub
8
9
0
04 Jul 2022
Concept Embedding Analysis: A Review
Concept Embedding Analysis: A Review
Gesina Schwalbe
19
28
0
25 Mar 2022
Visualizing Global Explanations of Point Cloud DNNs
Visualizing Global Explanations of Point Cloud DNNs
Hanxiao Tan
3DPC
34
7
0
17 Mar 2022
Efficient Decompositional Rule Extraction for Deep Neural Networks
Efficient Decompositional Rule Extraction for Deep Neural Networks
Mateo Espinosa Zarlenga
Z. Shams
M. Jamnik
14
16
0
24 Nov 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A
  Systematic Survey of Surveys on Methods and Concepts
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
Gesina Schwalbe
Bettina Finzel
XAI
21
184
0
15 May 2021
LionForests: Local Interpretation of Random Forests
LionForests: Local Interpretation of Random Forests
Ioannis Mollas
Nick Bassiliades
I. Vlahavas
Grigorios Tsoumakas
11
12
0
20 Nov 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies,
  Opportunities and Challenges toward Responsible AI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
S. Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
25
6,109
0
22 Oct 2019
Learning Causal State Representations of Partially Observable
  Environments
Learning Causal State Representations of Partially Observable Environments
Amy Zhang
Zachary Chase Lipton
Luis Villaseñor-Pineda
Kamyar Azizzadenesheli
Anima Anandkumar
Laurent Itti
Joelle Pineau
Tommaso Furlanello
CML
24
49
0
25 Jun 2019
Explaining Explanations: An Overview of Interpretability of Machine
  Learning
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin
David Bau
Ben Z. Yuan
Ayesha Bajwa
Michael A. Specter
Lalana Kagal
XAI
35
1,840
0
31 May 2018
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