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The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models.
What is a concept?
Dean R. Spitzer · 1975
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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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Testing and Reporting Performance Results of Cardiac Rhythm and ST-segment Measurement Algorithms
Association for the Advancement of Medical Instrumentation and American National Standards Institute · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A random walks view of spectral segmentation
Marina Meilă and Jianbo Shi · 2001
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The impact of the MIT-BIH arrhythmia database
George B Moody and Roger G Mark · 2001
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Clinician’s Pocket Reference
Steven A Haist · 2002
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Visual explanation of evidence with additive classifiers
Brett Poulin, Roman Eisner, Duane Szafron, Paul Lu, Russell Greiner, David S Wishart, Alona Fyshe, Brandon Pearcy, Cam MacDonell, and John Anvik · 2006
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A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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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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Learning to combine foveal glimpses with a third-order boltzmann machine
Hugo Larochelle and Geoffrey E Hinton · 2010
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Comprehensive electrocardiology
Peter W Macfarlane, Adriaan Van Oosterom, Olle Pahlm, Paul Kligfield, Michiel Janse, and John Camm · 2010
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Caltech-UCSD birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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Detecting rule of simplicity from photos
Long Mai, Hoang Le, Yuzhen Niu, Yu-Chi Lai, and Feng Liu · 2012
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Interpreting individual classifications of hierarchical networks
Will Landecker, Michael D Thomure, Luís MA Bettencourt, Melanie Mitchell, Garrett T Kenyon, and Steven P Brumby · 2013
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Age and gender estimation of unfiltered faces
Eran Eidinger, Roee Enbar, and Tal Hassner · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 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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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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The Romhilt-Estes left ventricular hypertrophy score and its components predict all-cause mortality in the general population
E Harvey Estes, Zhu-Ming Zhang, Yabing Li, Larisa G Tereschenko, and Elsayed Z Soliman · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 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, et al · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2015
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Object detectors emerge in deep scene CNNs
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
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Premature ventricular contractions: Reassure or refer?
Baris Akdemir, Hirad Yarmohammadi, M Chadi Alraies, and Wayne O Adkisson · 2016
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Towards better analysis of deep convolutional neural networks
Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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Anh Nguyen, Jason Yosinski, and Jeff Clune · 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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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2017
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The shattered gradients problem: If resnets are the answer, then what is the question?
David Balduzzi, Marcus Frean, Lennox Leary, J. P. Lewis, Kurt Wan-Duo Ma, and Brian McWilliams · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 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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Understanding and comparing deep neural networks for age and gender classification
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller, and Wojciech Samek · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Pruning convolutional neural networks for resource efficient inference
P Molchanov, S Tyree, T Karras, T Aila, and J Kautz · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Cited alongside, same era.
Learning to generate reviews and discovering sentiment
Alec Radford, Rafal Jozefowicz, and Ilya Sutskever · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Understanding the role of individual units in a deep neural network
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, and Antonio Torralba · 2020
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Notes on canonization for resnets and densenets
Alexander Binder · 2020
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Exemplary natural images explain CNN activations better than state-of-the-art feature visualization
Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas SA Wallis, Matthias Bethge, and Wieland Brendel · 2020
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Thread: Circuits
Nick Cammarata, Shan Carter, Gabriel Goh, Chris Olah, Michael Petrov, Ludwig Schubert, Chelsea Voss, Ben Egan, and Swee Kiat Lim · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Visualizing deep neural network decisions: Prediction difference analysis
Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling · 2017
Cited alongside, same era.
Peeking inside the black-box: A survey on explainable artificial intelligence
Amina Adadi and Mohammed Berrada · 2018
Cited alongside, same era.
Interpreting and explaining deep neural networks for classification of audio signals
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
Cited alongside, same era.
Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
Cited alongside, same era.
Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato, Brian Barr, and Claudio T Silva · 2020
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Opportunities and challenges in explainable artificial intelligence: A survey
Arun Das and Paul Rad · 2020
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Mathilde Guillemot, Catherine Heusele, Rodolphe Korichi, Sylvianne Schnebert, and Liming Chen · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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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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Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
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Expressive explanations of dnns by combining concept analysis with ilp
Johannes Rabold, Gesina Schwalbe, and Ute Schmid · 2020
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Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch, and Bin Yu · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
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Physically interpretable neural networks for the geosciences: Applications to earth system variability
Benjamin A Toms, Elizabeth A Barnes, and Imme Ebert-Uphoff · 2020
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Nbdt: Neural-backed decision trees
Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, and Joseph E Gonzalez · 2020
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On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin · 2021
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Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer
Hryhorii Chereda, Annalen Bleckmann, Kerstin Menck, Júlia Perera-Bel, Philip Stegmaier, Florian Auer, Frank Kramer, Andreas Leha, and Tim Beißbarth · 2021
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Coatnet: Marrying convolution and attention for all data sizes
Zihang Dai, Hanxiao Liu, Quoc Le, and Mingxing Tan · 2021
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Advancing mathematics by guiding human intuition with AI
Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomašev, Richard Tanburn, Peter Battaglia, Charles Blundell, András Juhász, et al · 2021
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Explanation as a process: user-centric construction of multi-level and multi-modal explanations
Bettina Finzel, David E Tafler, Stephan Scheele, and Ute Schmid · 2021
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Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah · 2021
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Explaining in style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T Freeman, Phillip Isola, Amir Globerson, Michal Irani, et al · 2021
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Explaining convolutional neural networks by tagging filters
Anna Nguyen, Daniel Hagenmayer, Tobias Weller, and Michael Färber · 2021
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Topoact: Visually exploring the shape of activations in deep learning
Archit Rathore, Nithin Chalapathi, Sourabh Palande, and Bei Wang · 2021
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Explaining deep neural networks and beyond: A review of methods and applications
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, and Klaus-Robert Müller · 2021
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Higher-order explanations of graph neural networks via relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2021
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Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, and Been Kim · 2021
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Pruning by explaining: A novel criterion for deep neural network pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2021
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Excitation dropout: Encouraging plasticity in deep neural networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio, Jianming Zhang, Stan Sclaroff, and Vittorio Murino · 2021
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Openxai: Towards a transparent evaluation of model explanations
Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju · 2022
Closest in time.
Finding and removing clever hans: Using explanation methods to debug and improve deep models
Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin · 2022
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ECQx: Explainability-driven quantization for low-bit and sparse dnns
Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, and Sebastian Lapuschkin · 2022
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Preddiff: Explanations and interactions from conditional expectations
Stefan Blücher, Johanna Vielhaben, and Nils Strodthoff · 2022
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Disentangled explanations of neural network predictions by finding relevant subspaces
Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller, and Grégoire Montavon · 2022
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But that’s not why: Inference adjustment by interactive prototype deselection
Michael Gerstenberger, Sebastian Lapuschkin, Peter Eisert, and Sebastian Bosse · 2022
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Varieties of ai explanations under the law. from the gdpr to the aia, and beyond
Philipp Hacker and Jan-Hendrik Passoth · 2022
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Natural language descriptions of deep visual features
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas · 2022
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Measurably stronger explanation reliability via model canonization
Franz Motzkus, Leander Weber, and Sebastian Lapuschkin · 2022
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Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome
Andreas Rieckmann, Piotr Dworzynski, Leila Arras, Sebastian Lapuschkin, Wojciech Samek, Onyebuchi A Arah, Naja H Rod, and Claus T Ekstrom · 2022
Closest in time.
Explain and improve: LRP-inference fine-tuning for image captioning models
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, and Alexander Binder · 2022
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rachtibat/zennit-crp: v0.6.0
Reduan Achtibat, Maximilian Dreyer, and Sebastian Lapuschkin · 2023
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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C Höhne · 2023
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Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
Johanna Vielhaben, Stefan Blücher, and Nils Strodthoff · 2023
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