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2111.07668
Cited By
Fast Axiomatic Attribution for Neural Networks
15 November 2021
Robin Hesse
Simone Schaub-Meyer
Stefan Roth
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Papers citing
"Fast Axiomatic Attribution for Neural Networks"
26 / 26 papers shown
Title
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable
Shreyash Arya
Sukrut Rao
Moritz Bohle
Bernt Schiele
161
3
0
28 Jan 2025
Robust Models Are More Interpretable Because Attributions Look Normal
Zifan Wang
Matt Fredrikson
Anupam Datta
OOD
FAtt
64
26
0
20 Mar 2021
Making deep neural networks right for the right scientific reasons by interacting with their explanations
P. Schramowski
Wolfgang Stammer
Stefano Teso
Anna Brugger
Xiaoting Shao
Hans-Georg Luigs
Anne-Katrin Mahlein
Kristian Kersting
104
212
0
15 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
121
6,269
0
22 Oct 2019
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger
Chandan Singh
W. James Murdoch
Bin Yu
FAtt
85
215
0
30 Sep 2019
Incorporating Priors with Feature Attribution on Text Classification
Frederick Liu
Besim Avci
FAtt
FaML
87
120
0
19 Jun 2019
Robust Attribution Regularization
Jiefeng Chen
Xi Wu
Vaibhav Rastogi
Yingyu Liang
S. Jha
OOD
50
83
0
23 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OOD
VLM
188
3,445
0
28 Mar 2019
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh
Cheng-Yu Hsieh
A. Suggala
David I. Inouye
Pradeep Ravikumar
FAtt
75
454
0
27 Jan 2019
Fixup Initialization: Residual Learning Without Normalization
Hongyi Zhang
Yann N. Dauphin
Tengyu Ma
ODL
AI4CE
91
351
0
27 Jan 2019
Hierarchical interpretations for neural network predictions
Chandan Singh
W. James Murdoch
Bin Yu
68
146
0
14 Jun 2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W. James Murdoch
Peter J. Liu
Bin Yu
78
210
0
16 Jan 2018
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
280
9,797
0
25 Oct 2017
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
203
3,879
0
10 Apr 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
A. Ross
M. C. Hughes
Finale Doshi-Velez
FAtt
126
591
0
10 Mar 2017
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
188
6,015
0
04 Mar 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
L. Zintgraf
Taco S. Cohen
T. Adel
Max Welling
FAtt
140
708
0
15 Feb 2017
Understanding Neural Networks through Representation Erasure
Jiwei Li
Will Monroe
Dan Jurafsky
AAML
MILM
91
567
0
24 Dec 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Shcherbina
A. Kundaje
FAtt
82
789
0
05 May 2016
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
326
18,625
0
06 Feb 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.7K
100,479
0
04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
1.7K
39,590
0
01 Sep 2014
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
246
16,373
0
30 Apr 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
312
7,308
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
595
15,893
0
12 Nov 2013
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