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GLIME: General, Stable and Local LIME Explanation

GLIME: General, Stable and Local LIME Explanation

Zeren Tan
Yang Tian
Jian Li
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Papers citing "GLIME: General, Stable and Local LIME Explanation"

18 / 18 papers shown
Title
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Anders Gjølbye
Stefan Haufe
Lars Kai Hansen
126
0
0
16 May 2025
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability
Wen-Dong Jiang
Chih-Yung Chang
Show-Jane Yen
Diptendu Sinha Roy
FAtt
HAI
178
1
0
02 Dec 2024
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
Tessa Han
Suraj Srinivas
Himabindu Lakkaraju
FAtt
57
86
0
02 Jun 2022
S-LIME: Stabilized-LIME for Model Explanation
S-LIME: Stabilized-LIME for Model Explanation
Zhengze Zhou
Giles Hooker
Fei Wang
FAtt
87
89
0
15 Jun 2021
What does LIME really see in images?
What does LIME really see in images?
Damien Garreau
Dina Mardaoui
FAtt
32
38
0
11 Feb 2021
Explaining by Removing: A Unified Framework for Model Explanation
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert
Scott M. Lundberg
Su-In Lee
FAtt
70
244
0
21 Nov 2020
Looking Deeper into Tabular LIME
Looking Deeper into Tabular LIME
Damien Garreau
U. V. Luxburg
FAtt
LMTD
139
30
0
25 Aug 2020
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms
OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms
Giorgio Visani
Enrico Bagli
F. Chesani
FAtt
34
60
0
10 Jun 2020
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models
Giorgio Visani
Enrico Bagli
F. Chesani
A. Poluzzi
D. Capuzzo
FAtt
22
166
0
31 Jan 2020
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
Wu Lin
Mohammad Emtiyaz Khan
Mark Schmidt
45
31
0
29 Oct 2019
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
538
21,613
0
22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong
Andrea Vedaldi
FAtt
AAML
48
1,514
0
11 Apr 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
123
3,848
0
10 Apr 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
115
5,920
0
04 Mar 2017
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Alexander Binder
G. Montavon
Sebastian Lapuschkin
K. Müller
Wojciech Samek
FAtt
54
456
0
04 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
582
16,828
0
16 Feb 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSL
SSeg
FAtt
163
9,266
0
14 Dec 2015
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
321
15,825
0
12 Nov 2013