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Deep learning is increasingly used in decision-making tasks.
Generalized fisheye views
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Visualizing higher-layer features of a deep network
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“search, show context, expand on demand”: supporting large graph exploration with degree-of-interest
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Generative adversarial nets
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Very deep convolutional networks for large-scale image recognition
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M. D. Zeiler and R. Fergus · 2014
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Modeltracker: Redesigning performance analysis tools for machine learning
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
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An interactive node-link visualization of convolutional neural networks
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Inceptionism: Going deeper into neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Image style transfer using convolutional neural networks
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Combining satellite imagery and machine learning to predict poverty
N. Jean, M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon · 2016
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The mythos of model interpretability
Z. C. Lipton · 2016
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General data protection regulation
Parliament and C. of the European Union · 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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Direct-manipulation visualization of deep networks
D. Smilkov, S. Carter, D. Sculley, F. B. Viégas, and M. Wattenberg · 2016
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Visualizing dataflow graphs of deep learning models in tensorflow
K. Wongsuphasawat, D. Smilkov, J. Wexler, J. Wilson, D. Mane, D. Fritz, D. Krishnan, F. B. Viégas, and M. Wattenberg · 2017
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Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda
A. Abdul, J. Vermeulen, D. Wang, B. Y. Lim, and M. Kankanhalli · 2018
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Do convolutional neural networks learn class hierarchy?
A. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren · 2018
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Shield: Fast, practical defense and vaccination for deep learning using jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
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Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
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Explanation and justification in machine learning: A survey
O. Biran and C. Cotton · 2017
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Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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Learning how to explain neural networks: Patternnet and patternattribution
P.-J. Kindermans, K. T. Schütt, M. Alber, K.-R. Müller, D. Erhan, B. Kim, and S. Dähne · 2017
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Towards better analysis of deep convolutional neural networks
M. Liu, J. Shi, Z. Li, C. Li, J. Zhu, and S. Liu · 2017
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R. Fong and A. Vedaldi · 2018
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Explaining explanations: An approach to evaluating interpretability of machine learning
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal · 2018
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Visual analytics in deep learning: An interrogative survey for the next frontiers
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2018
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Activis: Visual exploration of industry-scale deep neural network models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. P. Chau · 2018
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
B. Kim, W. M., J. Gilmer, C. C., W. J., , F. Viegas, and R. Sayres · 2018
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Analyzing the noise robustness of deep neural networks
M. Liu, S. Liu, H. Su, K. Cao, and J. Zhu · 2018
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
L. McInnes, J. Healy, and J. Melville · 2018
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The building blocks of interpretability
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, and A. Mordvintsev · 2018
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Choose your neuron: Incorporating domain knowledge through neuron-importance
R. R. Selvaraju, P. Chattopadhyay, M. Elhoseiny, T. Sharma, D. Batra, D. Parikh, and S. Lee · 2018
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Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer
D. F. Steiner, R. MacDonald, Y. Liu, P. Truszkowski, J. D. Hipp, C. Gammage, F. Thng, L. Peng, and M. C. Stumpe · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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Activation atlas
S. Carter, Z. Armstrong, L. Schubert, I. Johnson, and C. Olah · 2019
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Gamut: A design probe to understand how data scientists understand machine learning models
F. Hohman, A. Head, R. Caruana, R. DeLine, and S. M. Drucker · 2019
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Gan lab: Understanding complex deep generative models using interactive visual experimentation
M. Kahng, N. Thorat, D. H. P. Chau, F. B. Viégas, and M. Wattenberg · 2019
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Artificial intelligence-based breast cancer nodal metastasis detection
Y. Liu, T. Kohlberger, M. Norouzi, G. Dahl, J. Smith, A. Mohtashamian, N. Olson, L. Peng, J. Hipp, and M. Stumpe · 2019
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