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We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output.
Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee, R. B. Grosse, R. Ranganath, and A. Y. Ng · 2009
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A practical guide to training restricted boltzmann machines
G. E. Hinton · 2012
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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From image-level to pixel-level labeling with convolutional networks
P. H. O. Pinheiro and R. Collobert · 2015
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Improving the interpretability of deep neural networks with stimulated learning
S. Tan, K. C. Sim, and M. J. F. Gales · 2015
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Increasing the interpretability of recurrent neural networks using hidden markov models
V. Krakovna and F. Doshi-Velez · 2016
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Rationalizing neural predictions
T. Lei, R. Barzilay, and T. S. Jaakkola · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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"why should I trust you?": Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Deep learning for mortgage risk
J. Sirignano, A. Sadhwani, and K. Giesecke · 2016
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Pixel recurrent neural networks
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Stimulated deep neural network for speech recognition
C. Wu, P. Karanasou, M. J. F. Gales, and K. C. Sim · 2016
O. Li, H. Liu, C. Chen, and C. Rudin · 2017
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Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K. Müller · 2017
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cleverhans v2.0.0: an adversarial machine learning library
N. Papernot, N. Carlini, I. Goodfellow, R. Feinman, F. Faghri, A. Matyasko, K. Hambardzumyan, Y.-L. Juang, A. Kurakin, R. Sheatsley, A. Garg, and Y.-C. Lin · 2017
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Fannie Mae single-family loan performance data
Fannie Mae · 2018
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The Quick, Draw! Dataset
I. Google · 2018
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Interpretability via model extraction
O. Bastani, C. Kim, and H. Bastani · 2017
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A neural representation of sketch drawings
D. Ha and D. Eck · 2017
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Gurobi optimizer reference manual
Gurobi Optimization, Inc · 2018
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First-order theorem proving Data Set
S. B. H. James P Bridge and L. C. Paulson · 2018
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Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
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