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Explainable AI (XAI) is slowly becoming a key component for many AI applications.
Visualization of neural networks using saliency maps
Niels JS Morch, Ulrik Kjems, Lars Kai Hansen, Claus Svarer, Ian Law, Benny Lautrup, Steve Strother, and Kelly Rehm · 1995
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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An image is worth 16x16 words: Transformers for image recognition at scale. arxiv 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2010
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The Pascal Visual Object Classes (VOC) Challenge
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman · 2010
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A comparative assessment of ensemble learning for credit scoring
Gang Wang, Jinxing Hao, Jian Ma, and Hongbing Jiang · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sgdr: Stochastic gradient descent with warm restarts, 2016
Ilya Loshchilov and Frank Hutter · 2016
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
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” What is relevant in a text document?”: An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2017
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European Union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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On the (in) fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
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Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José M. F. Moura · 2020
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Concise explanations of neural networks using adversarial training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Xi Wu, and Somesh Jha · 2020
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Mathilde Guillemot, Catherine Heusele, Rodolphe Korichi, Sylvianne Schnebert, and Liming Chen · 2020
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Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Help wanted: An examination of hiring algorithms, equity, and bias
Miranda Bogen and Aaron Rieke · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Cited alongside, same era.
Towards best practice in explaining neural network decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin · 2020
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Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM
Farah Shahid, Aneela Zameer, and Muhammad Muneeb · 2020
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When explanations lie: Why many modified bp attributions fail
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2020
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Criminal justice, artificial intelligence systems, and human rights
Aleš Završnik · 2020
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Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin · 2021
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Openxai: Towards a transparent evaluation of model explanations
Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju · 2022
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CLEVR-XAI: a benchmark dataset for the ground truth evaluation of neural network explanations
Leila Arras, Ahmed Osman, and Wojciech Samek · 2022
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Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2022
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Quantus: an explainable ai toolkit for responsible evaluation of neural network explanations
Anna Hedström, Leander Weber, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C Höhne · 2022
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Measurably stronger explanation reliability via model canonization
Franz Motzkus, Leander Weber, and Sebastian Lapuschkin · 2022
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Beyond explaining: Opportunities and challenges of xai-based model improvement, 2022
Leander Weber, Sebastian Lapuschkin, Alexander Binder, and Wojciech Samek · 2022
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