Fetching the paper…
Reading the bibliography…
Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method.
The approximation of one matrix by another of lower rank
Carl Eckart and Gale Young · 1936
Earlier work this paper cites.
Minima of functions of several variables with inequalities as side constraints
William Karush · 1939
Earlier work this paper cites.
Nonlinear programming proceedings of the second berkeley symposium on mathematical statistics and probability
Harold W Kuhn and Albert W Tucker · 1951
Earlier work this paper cites.
Methods of conjugate gradients for solving
Magnus R Hestenes and Eduard Stiefel · 1952
Earlier work this paper cites.
Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. i theory
RI Cukier, CM Fortuin, Kurt E Shuler, AG Petschek, and J Ho Schaibly · 1973
Earlier work this paper cites.
Sensitivity analysis for non-linear mathematical models
Ilya M Sobol · 1993
Earlier work this paper cites.
Learning the parts of objects by non-negative matrix factorization
Daniel D Lee and H Sebastian Seung · 1999
Earlier work this paper cites.
Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates
Ilya M Sobol · 2001
Earlier work this paper cites.
The implicit function theorem: history, theory, and applications
Steven George Krantz and Harold R Parks · 2002
Earlier work this paper cites.
Random balance designs for the estimation of first order global sensitivity indices
Stefano Tarantola, Debora Gatelli, and Thierry Alex Mara · 2006
Earlier work this paper cites.
Algorithmic differentiation of implicit functions and optimal values
Bradley M Bell and James V Burke · 2008
Earlier work this paper cites.
Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
Earlier work this paper cites.
Fast local algorithms for large scale nonnegative matrix and tensor factorizations
Andrzej Cichocki and Anh-Huy Phan · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Calculations of sobol indices for the gaussian process metamodel
Amandine Marrel, Bertrand Iooss, Beatrice Laurent, and Olivier Roustant · 2009
Earlier work this paper cites.
Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index
Andrea Saltelli, Paola Annoni, Ivano Azzini, Francesca Campolongo, Marco Ratto, and Stefano Tarantola · 2010
Earlier work this paper cites.
On the complexity of nonnegative matrix factorization
Stephen A Vavasis · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
Earlier work this paper cites.
Algorithms for nonnegative matrix factorization with the
Cédric Févotte and Jérôme Idier · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
The moore–penrose pseudoinverse: A tutorial review of the theory
João Carlos Alves Barata and Mahir Saleh Hussein · 2012
Earlier work this paper cites.
Better estimation of small sobol’sensitivity indices
Art B Owen · 2013
Earlier work this paper cites.
Nonnegative matrix factorization: A comprehensive review
Yu-Xiong Wang and Yu-Jin Zhang · 2013
Earlier work this paper cites.
Asymptotic normality and efficiency of two sobol index estimators
Alexandre Janon, Thierry Klein, Agnes Lagnoux, Maëlle Nodet, and Clémentine Prieur · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Cited alongside, same era.
A review on global sensitivity analysis methods
Bertrand Iooss and Paul Lemaître · 2015
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
A flexible and efficient algorithmic framework for constrained matrix and tensor factorization
Kejun Huang, Nicholas D Sidiropoulos, and Athanasios P Liavas · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
Cited alongside, same era.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, XY Han, and David L Donoho · 2020
Later among the works it cites.
Using data imputation for signal separation in high-contrast imaging
Bin Ren, Laurent Pueyo, Christine Chen, Élodie Choquet, John H Debes, Gaspard Duchêne, François Ménard, and Marshall D Perrin · 2020
Later among the works it cites.
How useful are the machine-generated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels
Hua Shen and Ting-Hao Huang · 2020
Later among the works it cites.
When explanations lie: Why many modified bp attributions fail
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2020
Later among the works it cites.
Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Computer vision cracks the leaf code
Peter Wilf, Shengping Zhang, Sharat Chikkerur, Stefan A Little, Scott L Wing, and Thomas Serre · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
Cited alongside, same era.
Visualizing the impact of feature attribution baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2020
Later among the works it cites.
Invertible concept-based explanations for cnn models with non-negative concept activation vectors
Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A Ehinger, and Benjamin IP Rubinstein · 2020
Later among the works it cites.
Efficient and modular implicit differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert · 2021
Later among the works it cites.
What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods
Julien Colin, Thomas Fel, Rémi Cadène, and Thomas Serre · 2021
Later among the works it cites.
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Thomas Fel, Rémi Cadène, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre · 2021
Later among the works it cites.
A peek into the reasoning of neural networks: Interpreting with structural visual concepts
Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, and Ziyan Wu · 2021
Later among the works it cites.
On locality of local explanation models
Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, and Chris C Holmes · 2021
Later among the works it cites.
The out-of-distribution problem in explainability and search methods for feature importance explanations
Peter Hase, Harry Xie, and Mohit Bansal · 2021
Later among the works it cites.
On baselines for local feature attributions
Johannes Haug, Stefan Zürn, Peter El-Jiz, and Gjergji Kasneci · 2021
Later among the works it cites.
Evaluations and methods for explanation through robustness analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, and Cho-Jui Hsieh · 2021
Later among the works it cites.
Developments and applications of shapley effects to reliability-oriented sensitivity analysis with correlated inputs
Marouane Il Idrissi, Vincent Chabridon, and Bertrand Iooss · 2021
Later among the works it cites.
Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg · 2021
Later among the works it cites.
The right to contest ai
Margot E Kaminski and Jennifer M Urban · 2021
Later among the works it cites.
Eu artificial intelligence act: The european approach to ai
Mauritz Kop · 2021
Later among the works it cites.
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen · 2021
Later among the works it cites.
Toy models of superposition
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah · 2022
Closest in time.
Don’t lie to me! robust and efficient explainability with verified perturbation analysis
Thomas Fel, Mélanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodème, and Thomas Serre · 2022
Closest in time.
Xplique: A deep learning explainability toolbox
Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis Béthune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin, and Thomas Serre · 2022
Closest in time.
HIVE: Evaluating the human interpretability of visual explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky · 2022
Closest in time.
Making sense of dependence: Efficient black-box explanations using dependence measure
Paul Novello, Thomas Fel, and David Vigouroux · 2022
Closest in time.
Do users benefit from interpretable vision? a user study, baseline, and dataset
Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu, Philipp Weiß, and Tim Landgraf · 2022
Closest in time.