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This thesis explores advanced approaches to improve explainability in computer vision by analyzing and modeling the features exploited by deep neural networks.
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
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Analysis of variance designs for model output
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Learning the parts of objects by non-negative matrix factorization
Daniel D Lee and H Sebastian Seung · 1999
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Visual coding and the phase structure of natural scenes
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An overview of statistical learning theory
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The implementation of visual routines
P R Roelfsema, V A Lamme, and H Spekreijse · 2000
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Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates
I.M Sobol · 2001
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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The implicit function theorem: history, theory, and applications
Steven George Krantz and Harold R Parks · 2002
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Making best use of model evaluations to compute sensitivity indices
Andrea Saltelli · 2002
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Image phase or amplitude? rapid scene categorization is an amplitude-based process
Nathalie Guyader, Alan Chauvin, Carole Peyrin, Jeanny Hérault, and Christian Marendaz · 2004
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The similarity metric
Ming Li, Xin Chen, Xin Li, Bin Ma, and Paul MB Vitányi · 2004
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Distance-based classification with lipschitz functions
Ulrike von Luxburg and Olivier Bousquet · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Explanation: A mechanist alternative
William Bechtel and Adele Abrahamsen · 2005
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On the equivalence of nonnegative matrix factorization and spectral clustering
Chris Ding, Xiaofeng He, and Horst D Simon · 2005
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Models of bottom-up attention and saliency
L. Itti · 2005
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On global sensitivity analysis of quasi-monte carlo algorithms
IM Sobol’ and Sergei S Kucherenko · 2005
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Approximating rate-distortion graphs of individual data: Experiments in lossy compression and denoising
Steven de Rooij and Paul Vitányi · 2006
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Explanation and understanding
Frank C Keil · 2006
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Ultra-rapid object detection with saccadic eye movements: visual processing speed revisited
Holle Kirchner and Simon J Thorpe · 2006
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The structure and function of explanations
Tania Lombrozo · 2006
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Learning a dictionary of shape-components in visual cortex: Comparison with neurons, humans and machines
Thomas Serre · 2006
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Random balance designs for the estimation of first order global sensitivity indices
Stefano Tarantola, Debora Gatelli, and Thierry Alex Mara · 2006
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Rapid object categorization without conscious recognition: aneuropsychological study
Boucart Muriel, Thorpe Simon, and Kirschner Holle · 2007
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A feedforward architecture accounts for rapid categorization
Thomas Serre, Aude Oliva, and Tomaso Poggio · 2007
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A survey of explanations in recommender systems
Nava Tintarev and Judith Masthoff · 2007
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Algorithmic differentiation of implicit functions and optimal values
Bradley M Bell and James V Burke · 2008
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Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing
Alex Forsythe, Gerry Mulhern, and Martin Sawey · 2008
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Evaluating derivatives: principles and techniques of algorithmic differentiation
Andreas Griewank and Andrea Walther · 2008
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Algorithmic information theory
Peter D Grünwald, Paul MB Vitányi, et al · 2008
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Fast local algorithms for large scale nonnegative matrix and tensor factorizations
Andrzej Cichocki and Anh-Huy Phan · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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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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Visual complexity: is that all there is?
Alexandra Forsythe · 2009
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman · 2009
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Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise
Olivier R Joubert, Guillaume A Rousselet, Michele Fabre-Thorpe, and Denis Fize · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The role of visual salience in directing eye movements in visual object agnosia
Sabira K Mannan, Christopher Kennard, and Masud Husain · 2009
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Calculations of sobol indices for the gaussian process metamodel
Amandine Marrel, Bertrand Iooss, Beatrice Laurent, and Olivier Roustant · 2009
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Optimal transport: old and new , volume 338
Cédric Villani et al · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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MNIST handwritten digit database, 2010
Yann LeCun and Corinna Cortes · 2010
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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
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On the complexity of nonnegative matrix factorization
Stephen A Vavasis · 2010
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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
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Image complexity measure based on visual attention
Matthieur Perreira Da Silva, Vincent Courboulay, and Pascal Estraillier · 2011
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The characteristics and limits of rapid visual categorization
Michèle Fabre-Thorpe · 2011
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Algorithms for nonnegative matrix factorization with the
Cédric Févotte and Jérôme Idier · 2011
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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
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Adaptive deconvolutional networks for mid and high level feature learning
M. D. Zeiler, G. W. Taylor, and R. Fergus · 2011
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The moore–penrose pseudoinverse: A tutorial review of the theory
João Carlos Alves Barata and Mahir Saleh Hussein · 2012
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How does the brain solve visual object recognition?
James J DiCarlo, Davide Zoccolan, and Nicole C Rust · 2012
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Concept possession, experimental semantics, and hybrid theories of reference
James Genone and Tania Lombrozo · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Bias correction for the estimation of sensitivity indices based on random balance designs
Jean-Yves Tissot and Clémentine Prieur · 2012
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Efficient estimation of sensitivity indices
Sébastien Da Veiga and Fabrice Gamboa · 2013
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Better estimation of small sobol’sensitivity indices
Art B Owen · 2013
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Asymptotic normality and efficiency of two sobol index estimators
Alexandre Janon, Thierry Klein, Agnes Lagnoux, Maëlle Nodet, and Clémentine Prieur · 2014
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What do saliency models predict?
Kathryn Koehler, Fei Guo, Sheng Zhang, and Miguel P Eckstein · 2014
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Introduction to quasi-Monte Carlo integration and applications
Gunther Leobacher and Friedrich Pillichshammer · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Sparse modeling for image and vision processing
Julien Mairal, Francis Bach, Jean Ponce, et al · 2014
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K-sparse autoencoders
Alireza Makhzani and Brendan Frey · 2014
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Very deep convolutional networks for Large-Scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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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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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel L K Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Visualizing googlenet classes
M. Øygard Audun · 2015
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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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Global sensitivity analysis with dependence measures
Sebastien Da Veiga · 2015
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On integration methods based on scrambled nets of arbitrary size
Mathieu Gerber · 2015
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On the role of spatial phase and phase correlation in vision, illusion, and cognition
Evgeny Gladilin and Roland Eils · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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A review on global sensitivity analysis methods
Bertrand Iooss and Paul Lemaître · 2015
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SALICON: Saliency in context
Ming Jiang, Shengsheng Huang, Juanyong Duan, and Qi Zhao · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, and Klaus-Robert Müller · 2015
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Understanding intra-class knowledge inside cnn
Donglai Wei, Bolei Zhou, Antonio Torrabla, and William Freeman · 2015
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Layer normalization
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
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Incorporating nesterov momentum into adam
Timothy Dozat · 2016
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How deep is the feature analysis underlying rapid visual categorization?
Sven Eberhardt, Jonah G Cader, and Thomas Serre · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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psiturk: An open-source framework for conducting replicable behavioral experiments online
Todd M Gureckis, Jay Martin, John McDonnell, Alexander S Rich, Doug Markant, Anna Coenen, David Halpern, Jessica B Hamrick, and Patricia Chan · 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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A flexible and efficient algorithmic framework for constrained matrix and tensor factorization
Kejun Huang, Nicholas D Sidiropoulos, and Athanasios P Liavas · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
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Opportunities and challenges in explainable artificial intelligence (xai): A survey
Arun Das and Paul Rad · 2020
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Emergent properties of foveated perceptual systems
Arturo Deza and Talia Konkle · 2020
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Controversial stimuli: Pitting neural networks against each other as models of human cognition
Tal Golan, Prashant C Raju, and Nikolaus Kriegeskorte · 2020
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FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains
Jeanine Harb, Nicolas Rébéna, Raphaël Chosidow, Grégoire Roblin, Roman Potarusov, and Hatem Hajri · 2020
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Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Understanding neural networks through representation erasure, 2016
Jiwei Li, Will Monroe, and Dan Jurafsky · 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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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 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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Inception-v4, Inception-ResNet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alex Alemi · 2016
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Class visualization with bilateral filters. 2016
Mike Tyka · 2016
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Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal · 2020
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Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Disentangling neural mechanisms for perceptual grouping
Junkyung Kim*, Drew Linsley*, Kalpit Thakkar, and Thomas Serre · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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XNect: real-time multi-person 3D motion capture with a single RGB camera
Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Mohamed Elgharib, Pascal Fua, Hans-Peter Seidel, Helge Rhodin, Gerard Pons-Moll, and Christian Theobalt · 2020
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Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2020
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Generative causal explanations of black-box classifiers
Matthew O’Shaughnessy, Gregory Canal, Marissa Connor, Mark Davenport, and Christopher Rozell · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, XY Han, and David L Donoho · 2020
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Training robust neural networks using lipschitz bounds, 2020
Patricia Pauli, Anne Koch, Julian Berberich, and Frank Allgöwer · 2020
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A comprehensive comparison of total-order estimators for global sensitivity analysis
Arnald Puy, William Becker, Samuele Lo Piano, and Andrea Saltelli · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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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
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Irof: a low resource evaluation metric for explanation methods
Laura Rieger and Lars Kai Hansen · 2020
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Enriching ImageNet with human similarity judgments and psychological embeddings
Brett D Roads and Bradley C Love · 2020
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Do adversarially robust ImageNet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Achieving robustness in classification using optimal transport with hinge regularization, 2020
Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz, Thibaut Boissin, Jean-Michel Loubes, and Eustasio del Barrio · 2020
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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
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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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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
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Visualizing the impact of feature attribution baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2020
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al · 2020
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Automatic perturbation analysis for scalable certified robustness and beyond
Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, and Cho-Jui Hsieh · 2020
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ResNeSt: Split-Attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R Manmatha, Mu Li, and Alexander Smola · 2020
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Does explainable artificial intelligence improve human decision-making?
Yasmeen Alufaisan, Laura R Marusich, Jonathan Z Bakdash, Yan Zhou, and Murat Kantarcioglu · 2021
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Current challenges and future opportunities for xai in machine learning-based clinical decision support systems: a systematic review
Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A Becker, and Catherine Mooney · 2021
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Fairness seen as global sensitivity analysis
Clément Bénesse, Fabrice Gamboa, Jean-Michel Loubes, and Thibaut Boissin · 2021
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Numerical influence of relu’(0) on backpropagation
David Bertoin, Jérôme Bolte, Sébastien Gerchinovitz, and Edouard Pauwels · 2021
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Quality metrics for transparent machine learning with and without humans in the loop are not correlated
Felix Biessmann and Dionysius Refiano · 2021
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When pigs fly: Contextual reasoning in synthetic and natural scenes
P Bomatter, M Zhang, D Karev, and others · 2021
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Exemplary natural images explain cnn activations better than state-of-the-art feature visualization
Judy Borowski, Roland S Zimmermann, Judith Schepers, Robert Geirhos, Thomas SA Wallis, Matthias Bethge, and Wieland Brendel · 2021
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Asteryx: A model-agnostic sat-based approach for symbolic and score-based explanations
Ryma Boumazouza, Fahima Cheikh-Alili, Bertrand Mazure, and Karim Tabia · 2021
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CYBORG: Blending human saliency into the loss improves deep learning
Aidan Boyd, Patrick Tinsley, Kevin Bowyer, and Adam Czajka · 2021
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The many faces of 1-lipschitz neural networks, 2021
Louis Béthune, Alberto González-Sanz, Franck Mamalet, and Mathieu Serrurier · 2021
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Transformer tracking
Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, and Huchuan Lu · 2021
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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
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ConViT: Improving vision transformers with soft convolutional inductive biases
Stéphane d’Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, and Levent Sagun · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A Dosovitskiy, L Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, M Dehghani, Matthias Minderer, G Heigold, S Gelly, Jakob Uszkoreit, and N Houlsby · 2021
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Decomon: Automatic certified perturbation analysis of neural networks, 2021
Ducoffe, Melanie · 2021
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Explaining classifiers using adversarial perturbations on the perceptual ball
Andrew Elliott, Stephen Law, and Chris Russell · 2021
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Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre · 2021
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Coalitional strategies for efficient individual prediction explanation
Gabriel Ferrettini, Elodie Escriva, Julien Aligon, Jean-Baptiste Excoffier, and Chantal Soulé-Dupuy · 2021
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Res2Net: A new Multi-Scale backbone architecture
Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, and Philip Torr · 2021
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Partial success in closing the gap between human and machine vision
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2021
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Sharpening local interpretable model-agnostic explanations for histopathology: improved understandability and reliability
Mara Graziani, Iam Palatnik de Sousa, Marley MBR Vellasco, Eduardo Costa da Silva, Henning Müller, and Vincent Andrearczyk · 2021
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Oculo-retinal dynamics can explain the perception of minimal recognizable configurations
Liron Zipora Gruber, Shimon Ullman, and Ehud Ahissar · 2021
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Swag: Superpixels weighted by average gradients for explanations of cnns
Thomas Hartley, Kirill Sidorov, Christopher Willis, and David Marshall · 2021
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