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Layer-wise Relevance Propagation for Neural Networks with Local
  Renormalization Layers

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

4 April 2016
Alexander Binder
G. Montavon
Sebastian Lapuschkin
K. Müller
Wojciech Samek
    FAtt
ArXivPDFHTML

Papers citing "Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers"

19 / 69 papers shown
Title
SHAP values for Explaining CNN-based Text Classification Models
SHAP values for Explaining CNN-based Text Classification Models
Wei Zhao
Tarun Joshi
V. Nair
Agus Sudjianto
FAtt
10
36
0
26 Aug 2020
Sequential Explanations with Mental Model-Based Policies
Sequential Explanations with Mental Model-Based Policies
A. Yeung
Shalmali Joshi
Joseph Jay Williams
Frank Rudzicz
FAtt
LRM
31
15
0
17 Jul 2020
Attribution in Scale and Space
Attribution in Scale and Space
Shawn Xu
Subhashini Venugopalan
Mukund Sundararajan
FAtt
BDL
9
71
0
03 Apr 2020
Neuron Shapley: Discovering the Responsible Neurons
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani
James Zou
FAtt
TDI
25
108
0
23 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep
  Networks
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek
Pascal Sturmfels
Su-In Lee
FAtt
27
143
0
10 Feb 2020
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang
Jianye Hao
Guangyong Chen
Hongyao Tang
Yingfeng Chen
Yujing Hu
Changjie Fan
Zhongyu Wei
23
52
0
10 Feb 2020
Feature relevance quantification in explainable AI: A causal problem
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing
Lenon Minorics
Patrick Blobaum
FAtt
CML
11
278
0
29 Oct 2019
Interpreting Undesirable Pixels for Image Classification on Black-Box
  Models
Interpreting Undesirable Pixels for Image Classification on Black-Box Models
Sin-Han Kang
Hong G Jung
Seong-Whan Lee
FAtt
14
3
0
27 Sep 2019
Improving performance of deep learning models with axiomatic attribution
  priors and expected gradients
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
G. Erion
Joseph D. Janizek
Pascal Sturmfels
Scott M. Lundberg
Su-In Lee
OOD
BDL
FAtt
13
80
0
25 Jun 2019
Software and application patterns for explanation methods
Software and application patterns for explanation methods
Maximilian Alber
25
11
0
09 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really
  Learn
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin
S. Wäldchen
Alexander Binder
G. Montavon
Wojciech Samek
K. Müller
17
996
0
26 Feb 2019
DeepPINK: reproducible feature selection in deep neural networks
DeepPINK: reproducible feature selection in deep neural networks
Yang Young Lu
Yingying Fan
Jinchi Lv
William Stafford Noble
FAtt
19
124
0
04 Sep 2018
A Note about: Local Explanation Methods for Deep Neural Networks lack
  Sensitivity to Parameter Values
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values
Mukund Sundararajan
Ankur Taly
FAtt
11
21
0
11 Jun 2018
How Important Is a Neuron?
How Important Is a Neuron?
Kedar Dhamdhere
Mukund Sundararajan
Qiqi Yan
FAtt
GNN
14
128
0
30 May 2018
Did the Model Understand the Question?
Did the Model Understand the Question?
Pramod Kaushik Mudrakarta
Ankur Taly
Mukund Sundararajan
Kedar Dhamdhere
ELM
OOD
FAtt
27
196
0
14 May 2018
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,238
0
24 Jun 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
14
5,847
0
04 Mar 2017
Understanding intermediate layers using linear classifier probes
Understanding intermediate layers using linear classifier probes
Guillaume Alain
Yoshua Bengio
FAtt
34
891
0
05 Oct 2016
Identifying individual facial expressions by deconstructing a neural
  network
Identifying individual facial expressions by deconstructing a neural network
F. Arbabzadah
G. Montavon
K. Müller
Wojciech Samek
CVBM
FAtt
22
31
0
23 Jun 2016
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