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We present a novel methodology for repairing neural networks that use ReLU activation functions.
Basic properties of convex polytopes
Martin Henk, Jürgen Richter-Gebert, and Günter M. Ziegler · 1997
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On the complexity of vertex and facet enumeration for convex polytopes
David D Bremner · 1997
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The mnist database of handwritten digits
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Robust optimization , volume 28
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Bounding and counting linear regions of deep neural networks
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Reluplex: An efficient smt solver for verifying deep neural networks
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Tommaso Dreossi, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, and Sanjit A Seshia · 2018
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Mode: automated neural network model debugging via state differential analysis and input selection
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
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Kyle D Julian and Mykel J Kochenderfer · 2019
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Few-shot guided mix for dnn repairing
Xuhong Ren, Bing Yu, Hua Qi, Felix Juefei-Xu, Zhuo Li, Wanli Xue, Lei Ma, and Jianjun Zhao · 2020
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Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, and Artem Babenko · 2020
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Towards repairing neural networks correctly
Guoliang Dong, Jun Sun, Jingyi Wang, Xinyu Wang, and Ting Dai · 2020
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Minimal modifications of deep neural networks using verification
Ben Goldberger, Guy Katz, Yossi Adi, and Joseph Keshet · 2020
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Deep neural network compression for aircraft collision avoidance systems
Kyle D Julian, Mykel J Kochenderfer, and Michael P Owen · 2019
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Towards robust, locally linear deep networks
Guang-He Lee, David Alvarez-Melis, and Tommi S Jaakkola · 2019
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Provable repair of deep neural networks
Matthew Sotoudeh and Aditya V Thakur · 2021
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Gurobi Optimizer Reference Manual, 2021
Gurobi Optimization, LLC · 2021
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Natural adversarial examples
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