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We develop techniques to quantify the degree to which a given (training or testing) example is an outlier in the underlying distribution.
Selecting typical instances in instance-based learning
Jianping Zhang · 1992
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Evasion attacks against machine learning at test time
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The bayesian case model: A generative approach for case-based reasoning and prototype classification
Been Kim, Cynthia Rudin, and Julie A Shah · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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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 Bernstein, et al · 2015
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Deep learning with differential privacy
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Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Mentornet: Regularizing very deep neural networks on corrupted labels
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Biased importance sampling for deep neural network training
Angelos Katharopoulos and François Fleuret · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
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Training region-based object detectors with online hard example mining
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Practical coreset constructions for machine learning
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Learning to reweight examples for robust deep learning
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Coresets for monotonic functions with applications to deep learning
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