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Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini, Florian Tramèr, and Nicolas Papernot · 1903
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Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 1905
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Understanding the Limitations of Conditional Generative Models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, and Richard Zemel · 1906
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On a Measure of the Information Provided by an Experiment
D. V. Lindley · 1956
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
Min-Ling Zhang and Zhi-Hua Zhou · 2006
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Matplotlib: A 2D graphics environment
J. D. Hunter · 2007
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Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities
Zhou Wang and Eero P. Simoncelli · 2008
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 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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Torchvision the machine-vision package of torch
Sébastien Marcel and Yann Rodriguez · 2010
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Data Structures for Statistical Computing in Python
Wes McKinney · 2010
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statsmodels: Econometric and statistical modeling with python
Skipper Seabold and Josef Perktold · 2010
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Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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The NumPy Array: A Structure for Efficient Numerical Computation
S. van der Walt, S. C. Colbert, and G. Varoquaux · 2011
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Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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psiTurk (Version 1.02) [Software]
J.V. McDonnell, J.B. Martin, D.B. Markant, A. Coenen, A.S. Rich, and T.M. Gureckis · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A Toolbox for Representational Similarity Analysis
Hamed Nili, Cai Wingfield, Alexander Walther, Li Su, William Marslen-Wilson, and Nikolaus Kriegeskorte · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Seaborn: statistical data visualization
Michael Waskom, Olga Botvinnik, P Hobson, J Warmenhoven, JB Cole, Y Halchenko, J Vanderplas, S Hoyer, S Villalba, E Quintero, and others · 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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Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte · 2014
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Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
Nikolaus Kriegeskorte · 2015
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Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 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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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
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, Dan 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
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
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Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks
Rishi Rajalingham, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J. DiCarlo · 2018
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Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations
Joshua C. Peterson, Joshua T. Abbott, and Thomas L. Griffiths · 2018
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Deep Predictive Coding Network for Object Recognition
Haiguang Wen, Kuan Han, Junxing Shi, Yizhen Zhang, Eugenio Culurciello, and Zhongming Liu · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
Umut Güçlü and Marcel A. J. van Gerven · 2015
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Using goal-driven deep learning models to understand sensory cortex
Daniel L K Yamins and James J DiCarlo · 2016
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Deep Neural Networks as a Computational Model for Human Shape Sensitivity
Jonas Kubilius, Stefania Bracci, and Hans P. Op de Beeck · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, and Aude Oliva · 2016
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Seeing it all: Convolutional network layers map the function of the human visual system
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, and Bertrand Thirion · 2016
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Measuring the tendency of CNNs to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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Do Deep Generative Models Know What They Don’t Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2018
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DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules
Nicholas Frosst, Sara Sabour, and Geoffrey Hinton · 2018
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Deep Neural Networks in Computational Neuroscience
Tim C. Kietzmann, Patrick McClure, and Nikolaus Kriegeskorte · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Ruairidh M Battleday, Joshua C Peterson, and Thomas L Griffiths · 2019
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The spatiotemporal neural dynamics underlying perceived similarity for real-world objects
Radoslaw M. Cichy, Nikolaus Kriegeskorte, Kamila M. Jozwik, Jasper J. F. van den Bosch, and Ian Charest · 2019
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Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2019
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Robustness May Be at Odds with Accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
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Robustness (Python Library)
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, and Dimitris Tsipras · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, and James J. DiCarlo · 2020
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Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin, Nicholas Frosst, Sara Sabour, Colin Raffel, Garrison Cottrell, and Geoffrey Hinton · 2020
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Individual differences among deep neural network models
Johannes Mehrer, Courtney J. Spoerer, Nikolaus Kriegeskorte, and Tim C. Kietzmann · 2020
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Humans can decipher adversarial images
Zhenglong Zhou and Chaz Firestone · 2041
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