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Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples.
Clever Hans: (the horse of Mr. Von Osten.) a contribution to experimental animal and human psychology
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Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik · 2002
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Andrew Y Ng, Michael I Jordan, and Yair Weiss · 2002
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M Everingham, LV Gool, CKI Williams, J Winn, and A Zisserman · 2007
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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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Imagennet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Age and gender estimation of unfiltered faces
Eran Eidinger, Roee Enbar, and Tal Hassner · 2014
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Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation
Charlotte Soneson, Sarah Gerster, and Mauro Delorenzi · 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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 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 learning
Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, et al · 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 S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 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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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, et al · 2016
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’why should I trust you?’: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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General data protection regulation
Parliament and Council of the European Union · 2016
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Classifying and segmenting microscopy images with deep multiple instance learning
Oren Z. Kraus, Lei Jimmy Ba, and Brendan J. Frey · 2016
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cleverhans v1.0.0: an adversarial machine learning library
Nicolas Papernot, Ian Goodfellow, Ryan Sheatsley, Reuben Feinman, and Patrick McDaniel · 2016
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Analyzing classifiers: Fisher vectors and deep neural networks
Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2016
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Explaining therapy predictions with layer-wise relevance propagation in neural networks
Yinchong Yang, Volker Tresp, Marius Wunderle, and Peter A. Fasching · 2018
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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Beyond word importance: Contextual decomposition to extract interactions from lstms
W. James Murdoch, Peter J. Liu, and Bin Yu · 2018
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UMAP: uniform manifold approximation and projection for dimension reduction
Leland McInnes and John Healy · 2018
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Grandmaster level in starcraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
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Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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Beating the world’s best at super smash bros. with deep reinforcement learning
Vlad Firoiu, William F. Whitney, and Joshua B. Tenenbaum · 2017
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Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus-Robert Müller, and Alexandre Tkatchenko · 2017
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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
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
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